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recommendation system</title> <meta name="description" content="Search results for: recommendation system"> <meta name="keywords" content="recommendation system"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="recommendation system"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 17893</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: recommendation system</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17893</span> A Hybrid Recommendation System Based on Association Rules</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Mohammed%20Alsalama">Ahmed Mohammed Alsalama</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recommendation systems are widely used in e-commerce applications. The engine of a current recommendation system recommends items to a particular user based on user preferences and previous high ratings. Various recommendation schemes such as collaborative filtering and content-based approaches are used to build a recommendation system. Most of the current recommendation systems were developed to fit a certain domain such as books, articles, and movies. We propose a hybrid framework recommendation system to be applied on two-dimensional spaces (User x Item) with a large number of Users and a small number of Items. Moreover, our proposed framework makes use of both favorite and non-favorite items of a particular user. The proposed framework is built upon the integration of association rules mining and the content-based approach. The results of experiments show that our proposed framework can provide accurate recommendations to users. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=association%20rules" title=" association rules"> association rules</a>, <a href="https://publications.waset.org/abstracts/search?q=recommendation%20systems" title=" recommendation systems"> recommendation systems</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20systems" title=" hybrid systems"> hybrid systems</a> </p> <a href="https://publications.waset.org/abstracts/15279/a-hybrid-recommendation-system-based-on-association-rules" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15279.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">453</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17892</span> Societal Impacts of Algorithmic Recommendation System: Economy, International Relations, Political Ideologies, and Education</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maggie%20Shen">Maggie Shen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ever since the late 20th century, business giants have been competing to provide better experiences for their users. One way they strive to do so is through more efficiently connecting users with their goals, with recommendation systems that filter out unnecessary or less relevant information. Today’s top online platforms such as Amazon, Netflix, Airbnb, Tiktok, Facebook, and Google all utilize algorithmic recommender systems for different purposes—Product recommendation, movie recommendation, travel recommendation, relationship recommendation, etc. However, while bringing unprecedented convenience and efficiency, the prevalence of algorithmic recommendation systems also influences society in many ways. In using a variety of primary, secondary, and social media sources, this paper explores the impacts of algorithms, particularly algorithmic recommender systems, on different sectors of society. Four fields of interest will be specifically addressed in this paper: economy, international relations, political ideologies, and education. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=algorithms" title="algorithms">algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=economy" title=" economy"> economy</a>, <a href="https://publications.waset.org/abstracts/search?q=international%20relations" title=" international relations"> international relations</a>, <a href="https://publications.waset.org/abstracts/search?q=political%20ideologies" title=" political ideologies"> political ideologies</a>, <a href="https://publications.waset.org/abstracts/search?q=education" title=" education"> education</a> </p> <a href="https://publications.waset.org/abstracts/143723/societal-impacts-of-algorithmic-recommendation-system-economy-international-relations-political-ideologies-and-education" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143723.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">199</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17891</span> Application of Artificial Immune Systems Combined with Collaborative Filtering in Movie Recommendation System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pei-Chann%20Chang">Pei-Chann Chang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jhen-Fu%20Liao"> Jhen-Fu Liao</a>, <a href="https://publications.waset.org/abstracts/search?q=Chin-Hung%20Teng"> Chin-Hung Teng</a>, <a href="https://publications.waset.org/abstracts/search?q=Meng-Hui%20Chen"> Meng-Hui Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research combines artificial immune system with user and item based collaborative filtering to create an efficient and accurate recommendation system. By applying the characteristic of antibodies and antigens in the artificial immune system and using Pearson correlation coefficient as the affinity threshold to cluster the data, our collaborative filtering can effectively find useful users and items for rating prediction. This research uses MovieLens dataset as our testing target to evaluate the effectiveness of the algorithm developed in this study. The experimental results show that the algorithm can effectively and accurately predict the movie ratings. Compared to some state of the art collaborative filtering systems, our system outperforms them in terms of the mean absolute error on the MovieLens dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20immune%20system" title="artificial immune system">artificial immune system</a>, <a href="https://publications.waset.org/abstracts/search?q=collaborative%20filtering" title=" collaborative filtering"> collaborative filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=recommendation%20system" title=" recommendation system"> recommendation system</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity" title=" similarity"> similarity</a> </p> <a href="https://publications.waset.org/abstracts/5057/application-of-artificial-immune-systems-combined-with-collaborative-filtering-in-movie-recommendation-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5057.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">535</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17890</span> The Effects of Source and Timing on the Acceptance of New Product Recommendation: A Lab Experiment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yani%20Shi">Yani Shi</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiaqi%20Yan"> Jiaqi Yan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A new product is important for companies to extend consumers and manifest competitiveness. New product often involves new features that consumers might not be familiar with while it may also have a competitive advantage to attract consumers compared to established products. However, although most online retailers employ recommendation agents (RA) to influence consumers’ product choice decision, recommended new products are not accepted and chosen as expected. We argue that it might also be caused by providing a new product recommendation in the wrong way at the wrong time. This study seeks to discuss how new product evaluations sourced from third parties could be employed in RAs as evidence of the superiority for the new product and how the new product recommendation could be provided to a consumer at the right time so that it can be accepted and finally chosen during the consumer’s decision-making process. A 2*2 controlled laboratory experiment was conducted to understand the selection of new product recommendation sources and recommendation timing. Human subjects were randomly assigned to one of the four treatments to minimize the effects of individual differences on the results. Participants were told to make purchase choices from our product categories. We find that a new product recommended right after a similar existing product and with the source of the expert review will be more likely to be accepted. Based on this study, both theoretical and practical contributions are provided regarding new product recommendation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=new%20product%20recommendation" title="new product recommendation">new product recommendation</a>, <a href="https://publications.waset.org/abstracts/search?q=recommendation%20timing" title=" recommendation timing"> recommendation timing</a>, <a href="https://publications.waset.org/abstracts/search?q=recommendation%20source" title=" recommendation source"> recommendation source</a>, <a href="https://publications.waset.org/abstracts/search?q=recommendation%20agents" title=" recommendation agents"> recommendation agents</a> </p> <a href="https://publications.waset.org/abstracts/96996/the-effects-of-source-and-timing-on-the-acceptance-of-new-product-recommendation-a-lab-experiment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/96996.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">154</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17889</span> State of the Art on the Recommendation Techniques of Mobile Learning Activities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nassim%20Dennouni">Nassim Dennouni</a>, <a href="https://publications.waset.org/abstracts/search?q=Yvan%20Peter"> Yvan Peter</a>, <a href="https://publications.waset.org/abstracts/search?q=Luigi%20Lancieri"> Luigi Lancieri</a>, <a href="https://publications.waset.org/abstracts/search?q=Zohra%20Slama"> Zohra Slama</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The objective of this article is to make a bibliographic study on the recommendation of mobile learning activities that are used as part of the field trip scenarios. Indeed, the recommendation systems are widely used in the context of mobility because they can be used to provide learning activities. These systems should take into account the history of visits and teacher pedagogy to provide adaptive learning according to the instantaneous position of the learner. To achieve this objective, we review the existing literature on field trip scenarios to recommend mobile learning activities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mobile%20learning" title="mobile learning">mobile learning</a>, <a href="https://publications.waset.org/abstracts/search?q=field%20trip" title=" field trip"> field trip</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20learning%20activities" title=" mobile learning activities"> mobile learning activities</a>, <a href="https://publications.waset.org/abstracts/search?q=collaborative%20filtering" title=" collaborative filtering"> collaborative filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=recommendation%20system" title=" recommendation system"> recommendation system</a>, <a href="https://publications.waset.org/abstracts/search?q=point%20of%20interest" title=" point of interest"> point of interest</a>, <a href="https://publications.waset.org/abstracts/search?q=ACO%20algorithm" title=" ACO algorithm"> ACO algorithm</a> </p> <a href="https://publications.waset.org/abstracts/48290/state-of-the-art-on-the-recommendation-techniques-of-mobile-learning-activities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48290.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">446</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17888</span> User Intention Generation with Large Language Models Using Chain-of-Thought Prompting Title</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gangmin%20Li">Gangmin Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Fan%20Yang"> Fan Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Personalized recommendation is crucial for any recommendation system. One of the techniques for personalized recommendation is to identify the intention. Traditional user intention identification uses the user’s selection when facing multiple items. This modeling relies primarily on historical behaviour data resulting in challenges such as the cold start, unintended choice, and failure to capture intention when items are new. Motivated by recent advancements in Large Language Models (LLMs) like ChatGPT, we present an approach for user intention identification by embracing LLMs with Chain-of-Thought (CoT) prompting. We use the initial user profile as input to LLMs and design a collection of prompts to align the LLM's response through various recommendation tasks encompassing rating prediction, search and browse history, user clarification, etc. Our tests on real-world datasets demonstrate the improvements in recommendation by explicit user intention identification and, with that intention, merged into a user model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=personalized%20recommendation" title="personalized recommendation">personalized recommendation</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20user%20modelling" title=" generative user modelling"> generative user modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20intention%20identification" title=" user intention identification"> user intention identification</a>, <a href="https://publications.waset.org/abstracts/search?q=large%20language%20models" title=" large language models"> large language models</a>, <a href="https://publications.waset.org/abstracts/search?q=chain-of-thought%20prompting" title=" chain-of-thought prompting"> chain-of-thought prompting</a> </p> <a href="https://publications.waset.org/abstracts/185916/user-intention-generation-with-large-language-models-using-chain-of-thought-prompting-title" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185916.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">53</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17887</span> Context-Aware Recommender Systems Using User's Emotional State</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hoyeon%20Park">Hoyeon Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Kyoung-jae%20Kim"> Kyoung-jae Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The product recommendation is a field of research that has received much attention in the recent information overload phenomenon. The proliferation of the mobile environment and social media cannot help but affect the results of the recommendation depending on how the factors of the user's situation are reflected in the recommendation process. Recently, research has been spreading attention to the context-aware recommender system which is to reflect user's contextual information in the recommendation process. However, until now, most of the context-aware recommender system researches have been limited in that they reflect the passive context of users. It is expected that the user will be able to express his/her contextual information through his/her active behavior and the importance of the context-aware recommender system reflecting this information can be increased. The purpose of this study is to propose a context-aware recommender system that can reflect the user's emotional state as an active context information to recommendation process. The context-aware recommender system is a recommender system that can make more sophisticated recommendations by utilizing the user's contextual information and has an advantage that the user's emotional factor can be considered as compared with the existing recommender systems. In this study, we propose a method to infer the user's emotional state, which is one of the user's context information, by using the user's facial expression data and to reflect it on the recommendation process. This study collects the facial expression data of a user who is looking at a specific product and the user's product preference score. Then, we classify the facial expression data into several categories according to the previous research and construct a model that can predict them. Next, the predicted results are applied to existing collaborative filtering with contextual information. As a result of the study, it was shown that the recommended results of the context-aware recommender system including facial expression information show improved results in terms of recommendation performance. Based on the results of this study, it is expected that future research will be conducted on recommender system reflecting various contextual information. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=context-aware" title="context-aware">context-aware</a>, <a href="https://publications.waset.org/abstracts/search?q=emotional%20state" title=" emotional state"> emotional state</a>, <a href="https://publications.waset.org/abstracts/search?q=recommender%20systems" title=" recommender systems"> recommender systems</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20analytics" title=" business analytics"> business analytics</a> </p> <a href="https://publications.waset.org/abstracts/88567/context-aware-recommender-systems-using-users-emotional-state" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88567.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">229</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17886</span> Efficient Recommendation System for Frequent and High Utility Itemsets over Incremental Datasets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=J.%20K.%20Kavitha">J. K. Kavitha</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Manjula"> D. Manjula</a>, <a href="https://publications.waset.org/abstracts/search?q=U.%20Kanimozhi"> U. Kanimozhi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mining frequent and high utility item sets have gained much significance in the recent years. When the data arrives sporadically, incremental and interactive rule mining and utility mining approaches can be adopted to handle user’s dynamic environmental needs and avoid redundancies, using previous data structures, and mining results. The dependence on recommendation systems has exponentially risen since the advent of search engines. This paper proposes a model for building a recommendation system that suggests frequent and high utility item sets over dynamic datasets for a cluster based location prediction strategy to predict user’s trajectories using the Efficient Incremental Rule Mining (EIRM) algorithm and the Fast Update Utility Pattern Tree (FUUP) algorithm. Through comprehensive evaluations by experiments, this scheme has shown to deliver excellent performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20sets" title="data sets">data sets</a>, <a href="https://publications.waset.org/abstracts/search?q=recommendation%20system" title=" recommendation system"> recommendation system</a>, <a href="https://publications.waset.org/abstracts/search?q=utility%20item%20sets" title=" utility item sets"> utility item sets</a>, <a href="https://publications.waset.org/abstracts/search?q=frequent%20item%20sets%20mining" title=" frequent item sets mining"> frequent item sets mining</a> </p> <a href="https://publications.waset.org/abstracts/47560/efficient-recommendation-system-for-frequent-and-high-utility-itemsets-over-incremental-datasets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47560.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">293</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17885</span> Context-Aware Point-Of-Interests Recommender Systems Using Integrated Sentiment and Network Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ho%20Yeon%20Park">Ho Yeon Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Kyoung-Jae%20Kim"> Kyoung-Jae Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, user’s interests for location-based social network service increases according to the advances of social web and location-based technologies. It may be easy to recommend preferred items if we can use user’s preference, context and social network information simultaneously. In this study, we propose context-aware POI (point-of-interests) recommender systems using location-based network analysis and sentiment analysis which consider context, social network information and implicit user’s preference score. We propose a context-aware POI recommendation system consisting of three sub-modules and an integrated recommendation system of them. First, we will develop a recommendation module based on network analysis. This module combines social network analysis and cluster-indexing collaboration filtering. Next, this study develops a recommendation module using social singular value decomposition (SVD) and implicit SVD. In this research, we will develop a recommendation module that can recommend preference scores based on the frequency of POI visits of user in POI recommendation process by using social and implicit SVD which can reflect implicit feedback in collaborative filtering. We also develop a recommendation module using them that can estimate preference scores based on the recommendation. Finally, this study will propose a recommendation module using opinion mining and emotional analysis using data such as reviews of POIs extracted from location-based social networks. Finally, we will develop an integration algorithm that combines the results of the three recommendation modules proposed in this research. Experimental results show the usefulness of the proposed model in relation to the recommended performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sentiment%20analysis" title="sentiment analysis">sentiment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20analysis" title=" network analysis"> network analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=recommender%20systems" title=" recommender systems"> recommender systems</a>, <a href="https://publications.waset.org/abstracts/search?q=point-of-interests" title=" point-of-interests"> point-of-interests</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20analytics" title=" business analytics"> business analytics</a> </p> <a href="https://publications.waset.org/abstracts/72619/context-aware-point-of-interests-recommender-systems-using-integrated-sentiment-and-network-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72619.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">250</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17884</span> Developing a Recommendation Library System based on Android Application </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kunyanuth%20Kularbphettong">Kunyanuth Kularbphettong</a>, <a href="https://publications.waset.org/abstracts/search?q=Kunnika%20Tenprakhon"> Kunnika Tenprakhon</a>, <a href="https://publications.waset.org/abstracts/search?q=Pattarapan%20Roonrakwit"> Pattarapan Roonrakwit </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a recommendation library application on Android system. The objective of this system is to support and advice user to use library resources based on mobile application. We describe the design approaches and functional components of this system. The system was developed based on under association rules, Apriori algorithm. In this project, it was divided the result by the research purposes into 2 parts: developing the Mobile application for online library service and testing and evaluating the system. Questionnaires were used to measure user satisfaction with system usability by specialists and users. The results were satisfactory both specialists and users. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=online%20library" title="online library">online library</a>, <a href="https://publications.waset.org/abstracts/search?q=Apriori%20algorithm" title=" Apriori algorithm"> Apriori algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Android%20application" title=" Android application"> Android application</a>, <a href="https://publications.waset.org/abstracts/search?q=black%20box" title=" black box"> black box</a> </p> <a href="https://publications.waset.org/abstracts/7358/developing-a-recommendation-library-system-based-on-android-application" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7358.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">487</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17883</span> Recommendation Systems for Cereal Cultivation using Advanced Casual Inference Modeling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Md%20Yeasin">Md Yeasin</a>, <a href="https://publications.waset.org/abstracts/search?q=Ranjit%20Kumar%20Paul"> Ranjit Kumar Paul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, recommendation systems have become indispensable tools for agricultural system. The accurate and timely recommendations can significantly impact crop yield and overall productivity. Causal inference modeling aims to establish cause-and-effect relationships by identifying the impact of variables or factors on outcomes, enabling more accurate and reliable recommendations. New advancements in causal inference models have been found in the literature. With the advent of the modern era, deep learning and machine learning models have emerged as efficient tools for modeling. This study proposed an innovative approach to enhance recommendation systems-based machine learning based casual inference model. By considering the causal effect and opportunity cost of covariates, the proposed system can provide more reliable and actionable recommendations for cereal farmers. To validate the effectiveness of the proposed approach, experiments are conducted using cereal cultivation data of eastern India. Comparative evaluations are performed against existing correlation-based recommendation systems, demonstrating the superiority of the advanced causal inference modeling approach in terms of recommendation accuracy and impact on crop yield. Overall, it empowers farmers with personalized recommendations tailored to their specific circumstances, leading to optimized decision-making and increased crop productivity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agriculture" title="agriculture">agriculture</a>, <a href="https://publications.waset.org/abstracts/search?q=casual%20inference" title=" casual inference"> casual inference</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=recommendation%20system" title=" recommendation system"> recommendation system</a> </p> <a href="https://publications.waset.org/abstracts/169691/recommendation-systems-for-cereal-cultivation-using-advanced-casual-inference-modeling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169691.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">79</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17882</span> E-Learning Recommender System Based on Collaborative Filtering and Ontology</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=John%20Tarus">John Tarus</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhendong%20Niu"> Zhendong Niu</a>, <a href="https://publications.waset.org/abstracts/search?q=Bakhti%20Khadidja"> Bakhti Khadidja</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, e-learning recommender systems has attracted great attention as a solution towards addressing the problem of information overload in e-learning environments and providing relevant recommendations to online learners. E-learning recommenders continue to play an increasing educational role in aiding learners to find appropriate learning materials to support the achievement of their learning goals. Although general recommender systems have recorded significant success in solving the problem of information overload in e-commerce domains and providing accurate recommendations, e-learning recommender systems on the other hand still face some issues arising from differences in learner characteristics such as learning style, skill level and study level. Conventional recommendation techniques such as collaborative filtering and content-based deal with only two types of entities namely users and items with their ratings. These conventional recommender systems do not take into account the learner characteristics in their recommendation process. Therefore, conventional recommendation techniques cannot make accurate and personalized recommendations in e-learning environment. In this paper, we propose a recommendation technique combining collaborative filtering and ontology to recommend personalized learning materials to online learners. Ontology is used to incorporate the learner characteristics into the recommendation process alongside the ratings while collaborate filtering predicts ratings and generate recommendations. Furthermore, ontological knowledge is used by the recommender system at the initial stages in the absence of ratings to alleviate the cold-start problem. Evaluation results show that our proposed recommendation technique outperforms collaborative filtering on its own in terms of personalization and recommendation accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=collaborative%20filtering" title="collaborative filtering">collaborative filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=e-learning" title=" e-learning"> e-learning</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology" title=" ontology"> ontology</a>, <a href="https://publications.waset.org/abstracts/search?q=recommender%20system" title=" recommender system"> recommender system</a> </p> <a href="https://publications.waset.org/abstracts/64364/e-learning-recommender-system-based-on-collaborative-filtering-and-ontology" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64364.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">379</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17881</span> Rule-Based Expert System for Headache Diagnosis and Medication Recommendation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Noura%20Al-Ajmi">Noura Al-Ajmi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20A.%20Almulla"> Mohammed A. Almulla</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the increased utilization of technology devices around the world, healthcare and medical diagnosis are critical issues that people worry about these days. Doctors are doing their best to avoid any medical errors while diagnosing diseases and prescribing the wrong medication. Subsequently, artificial intelligence applications that can be installed on mobile devices such as rule-based expert systems facilitate the task of assisting doctors in several ways. Due to their many advantages, the usage of expert systems has increased recently in health sciences. This work presents a backward rule-based expert system that can be used for a headache diagnosis and medication recommendation system. The structure of the system consists of three main modules, namely the input unit, the processing unit, and the output unit. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=headache%20diagnosis%20system" title="headache diagnosis system">headache diagnosis system</a>, <a href="https://publications.waset.org/abstracts/search?q=prescription%20recommender%20system" title=" prescription recommender system"> prescription recommender system</a>, <a href="https://publications.waset.org/abstracts/search?q=expert%20system" title=" expert system"> expert system</a>, <a href="https://publications.waset.org/abstracts/search?q=backward%20rule-based%20system" title=" backward rule-based system"> backward rule-based system</a> </p> <a href="https://publications.waset.org/abstracts/125207/rule-based-expert-system-for-headache-diagnosis-and-medication-recommendation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/125207.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">215</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17880</span> Combination Urea and KCl with Powder Coal Sub-Bituminous to Increase Nutrient Content of Ultisols in Limau Manis Padang West Sumatra</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amsar%20Maulana">Amsar Maulana</a>, <a href="https://publications.waset.org/abstracts/search?q=Rafdea%20Syafitri"> Rafdea Syafitri</a>, <a href="https://publications.waset.org/abstracts/search?q=Topanal%20%20Gustiranda"> Topanal Gustiranda</a>, <a href="https://publications.waset.org/abstracts/search?q=Natasya%20Permatasari"> Natasya Permatasari</a>, <a href="https://publications.waset.org/abstracts/search?q=Herviyanti"> Herviyanti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Coal as an alternative source of humic material that has the potential of 973.92 million tons (sub-bituminous amounted to 673.70 million tons) in West Sumatera. The purpose of this research was to study combination Urea and KCl with powder coal Sub-bituminous to increase nutrient content of Ultisols In Limau Manis Padang West Sumatera. The experiment was designed in Completely Randomized Design with 3 replications, those were T1) 0.5% (50g plot-1) of powder coal Sub-bituminous; T2) T1 and 125% (7.03g plot-1 ) of Urea recommendation; T3) T1 and 125% (5.85g plot-1) of KCl recommendation; T4) 1.0% (100g plot-1) of powder coal Sub-bituminous; T5) T4 and 125% (7.03g plot-1 ) of Urea recommendation; T6) T4 and 125% (5.85g plot-1) of KCl recommendation; T7) 1.5% (150g plot-1) of powder coal Sub-bituminous; T8) T7 and 125% (7.03g plot-1 ) of Urea recommendation; T9) T7 and 125% (5.85g plot-1) of KCl recommendation. The results showed that application 1.5% of powder coal Sub-bituminous and 125% of Urea recommendation could increase nutrient content of Ultisols such as pH by 0.33 unit, Organic – C by 2.03%, total – N by 0.31%, Available P by 14.16 ppm and CEC by 19.38 me 100g-1 after 2 weeks of incubation process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=KCl" title="KCl">KCl</a>, <a href="https://publications.waset.org/abstracts/search?q=sub-bituminous" title=" sub-bituminous"> sub-bituminous</a>, <a href="https://publications.waset.org/abstracts/search?q=ultisols" title=" ultisols"> ultisols</a>, <a href="https://publications.waset.org/abstracts/search?q=urea" title=" urea"> urea</a> </p> <a href="https://publications.waset.org/abstracts/67115/combination-urea-and-kcl-with-powder-coal-sub-bituminous-to-increase-nutrient-content-of-ultisols-in-limau-manis-padang-west-sumatra" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67115.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">264</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17879</span> A Case Study for User Rating Prediction on Automobile Recommendation System Using Mapreduce</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jiao%20Sun">Jiao Sun</a>, <a href="https://publications.waset.org/abstracts/search?q=Li%20Pan"> Li Pan</a>, <a href="https://publications.waset.org/abstracts/search?q=Shijun%20Liu"> Shijun Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recommender systems have been widely used in contemporary industry, and plenty of work has been done in this field to help users to identify items of interest. Collaborative Filtering (CF, for short) algorithm is an important technology in recommender systems. However, less work has been done in automobile recommendation system with the sharp increase of the amount of automobiles. What’s more, the computational speed is a major weakness for collaborative filtering technology. Therefore, using MapReduce framework to optimize the CF algorithm is a vital solution to this performance problem. In this paper, we present a recommendation of the users’ comment on industrial automobiles with various properties based on real world industrial datasets of user-automobile comment data collection, and provide recommendation for automobile providers and help them predict users’ comment on automobiles with new-coming property. Firstly, we solve the sparseness of matrix using previous construction of score matrix. Secondly, we solve the data normalization problem by removing dimensional effects from the raw data of automobiles, where different dimensions of automobile properties bring great error to the calculation of CF. Finally, we use the MapReduce framework to optimize the CF algorithm, and the computational speed has been improved times. UV decomposition used in this paper is an often used matrix factorization technology in CF algorithm, without calculating the interpolation weight of neighbors, which will be more convenient in industry. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=collaborative%20filtering" title="collaborative filtering">collaborative filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=recommendation" title=" recommendation"> recommendation</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20normalization" title=" data normalization"> data normalization</a>, <a href="https://publications.waset.org/abstracts/search?q=mapreduce" title=" mapreduce"> mapreduce</a> </p> <a href="https://publications.waset.org/abstracts/54090/a-case-study-for-user-rating-prediction-on-automobile-recommendation-system-using-mapreduce" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54090.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">217</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17878</span> Multi-Stream Graph Attention Network for Recommendation with Knowledge Graph</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhifei%20Hu">Zhifei Hu</a>, <a href="https://publications.waset.org/abstracts/search?q=Feng%20Xia"> Feng Xia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, Graph neural network has been widely used in knowledge graph recommendation. The existing recommendation methods based on graph neural network extract information from knowledge graph through entity and relation, which may not be efficient in the way of information extraction. In order to better propose useful entity information for the current recommendation task in the knowledge graph, we propose an end-to-end Neural network Model based on multi-stream graph attentional Mechanism (MSGAT), which can effectively integrate the knowledge graph into the recommendation system by evaluating the importance of entities from both users and items. Specifically, we use the attention mechanism from the user's perspective to distil the domain nodes information of the predicted item in the knowledge graph, to enhance the user's information on items, and generate the feature representation of the predicted item. Due to user history, click items can reflect the user's interest distribution, we propose a multi-stream attention mechanism, based on the user's preference for entities and relationships, and the similarity between items to be predicted and entities, aggregate user history click item's neighborhood entity information in the knowledge graph and generate the user's feature representation. We evaluate our model on three real recommendation datasets: Movielens-1M (ML-1M), LFM-1B 2015 (LFM-1B), and Amazon-Book (AZ-book). Experimental results show that compared with the most advanced models, our proposed model can better capture the entity information in the knowledge graph, which proves the validity and accuracy of the model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=graph%20attention%20network" title="graph attention network">graph attention network</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20graph" title=" knowledge graph"> knowledge graph</a>, <a href="https://publications.waset.org/abstracts/search?q=recommendation" title=" recommendation"> recommendation</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20propagation" title=" information propagation"> information propagation</a> </p> <a href="https://publications.waset.org/abstracts/150710/multi-stream-graph-attention-network-for-recommendation-with-knowledge-graph" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150710.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">116</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17877</span> Crop Recommendation System Using Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prathik%20Ranka">Prathik Ranka</a>, <a href="https://publications.waset.org/abstracts/search?q=Sridhar%20K"> Sridhar K</a>, <a href="https://publications.waset.org/abstracts/search?q=Vasanth%20Daniel"> Vasanth Daniel</a>, <a href="https://publications.waset.org/abstracts/search?q=Mithun%20Shankar"> Mithun Shankar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With growing global food needs and climate uncertainties, informed crop choices are critical for increasing agricultural productivity. Here we propose a machine learning-based crop recommendation system to help farmers in choosing the most proper crops according to their geographical regions and soil properties. We can deploy algorithms like Decision Trees, Random Forests and Support Vector Machines on a broad dataset that consists of climatic factors, soil characteristics and historical crop yields to predict the best choice of crops. The approach includes first preprocessing the data after assessing them for missing values, unlike in previous jobs where we used all the available information and then transformed because there was no way such a model could have worked with missing data, and normalizing as throughput that will be done over a network to get best results out of our machine learning division. The model effectiveness is measured through performance metrics like accuracy, precision and recall. The resultant app provides a farmer-friendly dashboard through which farmers can enter their local conditions and receive individualized crop suggestions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=crop%20recommendation" title="crop recommendation">crop recommendation</a>, <a href="https://publications.waset.org/abstracts/search?q=precision%20agriculture" title=" precision agriculture"> precision agriculture</a>, <a href="https://publications.waset.org/abstracts/search?q=crop" title=" crop"> crop</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/193115/crop-recommendation-system-using-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193115.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">14</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17876</span> A Survey on Speech Emotion-Based Music Recommendation System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chirag%20Kothawade">Chirag Kothawade</a>, <a href="https://publications.waset.org/abstracts/search?q=Gourie%20Jagtap"> Gourie Jagtap</a>, <a href="https://publications.waset.org/abstracts/search?q=PreetKaur%20Relusinghani"> PreetKaur Relusinghani</a>, <a href="https://publications.waset.org/abstracts/search?q=Vedang%20Chavan"> Vedang Chavan</a>, <a href="https://publications.waset.org/abstracts/search?q=Smitha%20S.%20Bhosale"> Smitha S. Bhosale</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Psychological research has proven that music relieves stress, elevates mood, and is responsible for the release of “feel-good” chemicals like oxytocin, serotonin, and dopamine. It comes as no surprise that music has been a popular tool in rehabilitation centers and therapy for various disorders, thus with the interminably rising numbers of people facing mental health-related issues across the globe, addressing mental health concerns is more crucial than ever. Despite the existing music recommendation systems, there is a dearth of holistically curated algorithms that take care of the needs of users. Given that, an undeniable majority of people turn to music on a regular basis and that music has been proven to increase cognition, memory, and sleep quality while reducing anxiety, pain, and blood pressure, it is the need of the hour to fashion a product that extracts all the benefits of music in the most extensive and deployable method possible. Our project aims to ameliorate our users’ mental state by building a comprehensive mood-based music recommendation system called “Viby”. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=language" title="language">language</a>, <a href="https://publications.waset.org/abstracts/search?q=communication" title=" communication"> communication</a>, <a href="https://publications.waset.org/abstracts/search?q=speech%20recognition" title=" speech recognition"> speech recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=interaction" title=" interaction"> interaction</a> </p> <a href="https://publications.waset.org/abstracts/177086/a-survey-on-speech-emotion-based-music-recommendation-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/177086.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">63</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17875</span> Design of Personal Job Recommendation Framework on Smartphone Platform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chayaporn%20Kaensar">Chayaporn Kaensar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, Job Recommender Systems have gained much attention in industries since they solve the problem of information overload on the recruiting website. Therefore, we proposed Extended Personalized Job System that has the capability of providing the appropriate jobs for job seeker and recommending some suitable information for them using Data Mining Techniques and Dynamic User Profile. On the other hands, company can also interact to the system for publishing and updating job information. This system have emerged and supported various platforms such as web application and android mobile application. In this paper, User profiles, Implicit User Action, User Feedback, and Clustering Techniques in WEKA libraries have gained attention and implemented for this application. In additions, open source tools like Yii Web Application Framework, Bootstrap Front End Framework and Android Mobile Technology were also applied. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=recommendation" title="recommendation">recommendation</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20profile" title=" user profile"> user profile</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20and%20mobile%20technology" title=" web and mobile technology"> web and mobile technology</a> </p> <a href="https://publications.waset.org/abstracts/41448/design-of-personal-job-recommendation-framework-on-smartphone-platform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41448.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">313</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17874</span> Outline of a Technique for the Recommendation of Tourism Products in Cuba Using GIS</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jesse%20D.%20Cano">Jesse D. Cano</a>, <a href="https://publications.waset.org/abstracts/search?q=Marlon%20J.%20Remedios"> Marlon J. Remedios</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cuban tourism has developed so much in the last 30 years to the point of becoming one of the engines of the Cuban economy. With such a development, Cuban companies opting for e-tourism as a way to publicize their products and attract customers has also grown. Despite this fact, the majority of Cuban tourism-themed websites simply provide information on the different products and services they offer which results in many cases, in the user getting overwhelmed with the amount of information available which results in the user abandoning the search before he can find a product that fits his needs. Customization has been recognized as a critical factor for successful electronic tourism business and the use of recommender systems is the best approach to address the problem of personalization. This paper aims to outline a preliminary technique to obtain predictions about which products a particular user would give a better evaluation; these products would be those which the website would show in the first place. To achieve this, the theoretical elements of the Cuban tourism environment are discussed; recommendation systems and geographic information systems as tools for information representation are also discussed. Finally, for each structural component identified, we define a set of rules that allows obtaining an electronic tourism system that handles the personalization of the service provided effectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=geographic%20information%20system" title="geographic information system">geographic information system</a>, <a href="https://publications.waset.org/abstracts/search?q=technique" title=" technique"> technique</a>, <a href="https://publications.waset.org/abstracts/search?q=tourism%20products" title=" tourism products"> tourism products</a>, <a href="https://publications.waset.org/abstracts/search?q=recommendation" title=" recommendation"> recommendation</a> </p> <a href="https://publications.waset.org/abstracts/20202/outline-of-a-technique-for-the-recommendation-of-tourism-products-in-cuba-using-gis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20202.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">503</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17873</span> Book Exchange System with a Hybrid Recommendation Engine</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nilki%20Upathissa">Nilki Upathissa</a>, <a href="https://publications.waset.org/abstracts/search?q=Torin%20Wirasinghe"> Torin Wirasinghe</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This solution addresses the challenges faced by traditional bookstores and the limitations of digital media, striking a balance between the tactile experience of printed books and the convenience of modern technology. The book exchange system offers a sustainable alternative, empowering users to access a diverse range of books while promoting community engagement. The user-friendly interfaces incorporated into the book exchange system ensure a seamless and enjoyable experience for users. Intuitive features for book management, search, and messaging facilitate effortless exchanges and interactions between users. By streamlining the process, the system encourages readers to explore new books aligned with their interests, enhancing the overall reading experience. Central to the system's success is the hybrid recommendation engine, which leverages advanced technologies such as Long Short-Term Memory (LSTM) models. By analyzing user input, the engine accurately predicts genre preferences, enabling personalized book recommendations. The hybrid approach integrates multiple technologies, including user interfaces, machine learning models, and recommendation algorithms, to ensure the accuracy and diversity of the recommendations. The evaluation of the book exchange system with the hybrid recommendation engine demonstrated exceptional performance across key metrics. The high accuracy score of 0.97 highlights the system's ability to provide relevant recommendations, enhancing users' chances of discovering books that resonate with their interests. The commendable precision, recall, and F1score scores further validate the system's efficacy in offering appropriate book suggestions. Additionally, the curve classifications substantiate the system's effectiveness in distinguishing positive and negative recommendations. This metric provides confidence in the system's ability to navigate the vast landscape of book choices and deliver recommendations that align with users' preferences. Furthermore, the implementation of this book exchange system with a hybrid recommendation engine has the potential to revolutionize the way readers interact with printed books. By facilitating book exchanges and providing personalized recommendations, the system encourages a sense of community and exploration within the reading community. Moreover, the emphasis on sustainability aligns with the growing global consciousness towards eco-friendly practices. With its robust technical approach and promising evaluation results, this solution paves the way for a more inclusive, accessible, and enjoyable reading experience for book lovers worldwide. In conclusion, the developed book exchange system with a hybrid recommendation engine represents a progressive solution to the challenges faced by traditional bookstores and the limitations of digital media. By promoting sustainability, widening access to printed books, and fostering engagement with reading, this system addresses the evolving needs of book enthusiasts. The integration of user-friendly interfaces, advanced machine learning models, and recommendation algorithms ensure accurate and diverse book recommendations, enriching the reading experience for users. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=recommendation%20systems" title="recommendation systems">recommendation systems</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20recommendation%20systems" title=" hybrid recommendation systems"> hybrid recommendation systems</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20science" title=" data science"> data science</a>, <a href="https://publications.waset.org/abstracts/search?q=long%20short-term%20memory" title=" long short-term memory"> long short-term memory</a>, <a href="https://publications.waset.org/abstracts/search?q=recurrent%20neural%20network" title=" recurrent neural network"> recurrent neural network</a> </p> <a href="https://publications.waset.org/abstracts/167891/book-exchange-system-with-a-hybrid-recommendation-engine" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167891.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">94</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17872</span> A Study of the Performance Parameter for Recommendation Algorithm Evaluation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=C.%20Rana">C. Rana</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20K.%20Jain"> S. K. Jain </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The enormous amount of Web data has challenged its usage in efficient manner in the past few years. As such, a range of techniques are applied to tackle this problem; prominent among them is personalization and recommender system. In fact, these are the tools that assist user in finding relevant information of web. Most of the e-commerce websites are applying such tools in one way or the other. In the past decade, a large number of recommendation algorithms have been proposed to tackle such problems. However, there have not been much research in the evaluation criteria for these algorithms. As such, the traditional accuracy and classification metrics are still used for the evaluation purpose that provides a static view. This paper studies how the evolution of user preference over a period of time can be mapped in a recommender system using a new evaluation methodology that explicitly using time dimension. We have also presented different types of experimental set up that are generally used for recommender system evaluation. Furthermore, an overview of major accuracy metrics and metrics that go beyond the scope of accuracy as researched in the past few years is also discussed in detail. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=collaborative%20filtering" title="collaborative filtering">collaborative filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary" title=" evolutionary"> evolutionary</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithm" title=" algorithm"> algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=recommender%20systems" title=" recommender systems "> recommender systems </a> </p> <a href="https://publications.waset.org/abstracts/3921/a-study-of-the-performance-parameter-for-recommendation-algorithm-evaluation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3921.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">413</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17871</span> Deep Learning for Recommender System: Principles, Methods and Evaluation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Basiliyos%20Tilahun%20Betru">Basiliyos Tilahun Betru</a>, <a href="https://publications.waset.org/abstracts/search?q=Charles%20Awono%20Onana"> Charles Awono Onana</a>, <a href="https://publications.waset.org/abstracts/search?q=Bernabe%20Batchakui"> Bernabe Batchakui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recommender systems have become increasingly popular in recent years, and are utilized in numerous areas. Nowadays many web services provide several information for users and recommender systems have been developed as critical element of these web applications to predict choice of preference and provide significant recommendations. With the help of the advantage of deep learning in modeling different types of data and due to the dynamic change of user preference, building a deep model can better understand users demand and further improve quality of recommendation. In this paper, deep neural network models for recommender system are evaluated. Most of deep neural network models in recommender system focus on the classical collaborative filtering user-item setting. Deep learning models demonstrated high level features of complex data can be learned instead of using metadata which can significantly improve accuracy of recommendation. Even though deep learning poses a great impact in various areas, applying the model to a recommender system have not been fully exploited and still a lot of improvements can be done both in collaborative and content-based approach while considering different contextual factors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20making" title=" decision making"> decision making</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=recommender%20system" title=" recommender system"> recommender system</a> </p> <a href="https://publications.waset.org/abstracts/74244/deep-learning-for-recommender-system-principles-methods-and-evaluation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74244.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">478</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17870</span> Destination Decision Model for Cruising Taxis Based on Embedding Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kazuki%20Kamada">Kazuki Kamada</a>, <a href="https://publications.waset.org/abstracts/search?q=Haruka%20Yamashita"> Haruka Yamashita</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Japan, taxi is one of the popular transportations and taxi industry is one of the big businesses. However, in recent years, there has been a difficult problem of reducing the number of taxi drivers. In the taxi business, mainly three passenger catching methods are applied. One style is "cruising" that drivers catches passengers while driving on a road. Second is "waiting" that waits passengers near by the places with many requirements for taxies such as entrances of hospitals, train stations. The third one is "dispatching" that is allocated based on the contact from the taxi company. Above all, the cruising taxi drivers need the experience and intuition for finding passengers, and it is difficult to decide "the destination for cruising". The strong recommendation system for the cruising taxies supports the new drivers to find passengers, and it can be the solution for the decreasing the number of drivers in the taxi industry. In this research, we propose a method of recommending a destination for cruising taxi drivers. On the other hand, as a machine learning technique, the embedding models that embed the high dimensional data to a low dimensional space is widely used for the data analysis, in order to represent the relationship of the meaning between the data clearly. Taxi drivers have their favorite courses based on their experiences, and the courses are different for each driver. We assume that the course of cruising taxies has meaning such as the course for finding business man passengers (go around the business area of the city of go to main stations) and course for finding traveler passengers (go around the sightseeing places or big hotels), and extract the meaning of their destinations. We analyze the cruising history data of taxis based on the embedding model and propose the recommendation system for passengers. Finally, we demonstrate the recommendation of destinations for cruising taxi drivers based on the real-world data analysis using proposing method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=taxi%20industry" title="taxi industry">taxi industry</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20making" title=" decision making"> decision making</a>, <a href="https://publications.waset.org/abstracts/search?q=recommendation%20system" title=" recommendation system"> recommendation system</a>, <a href="https://publications.waset.org/abstracts/search?q=embedding%20model" title=" embedding model"> embedding model</a> </p> <a href="https://publications.waset.org/abstracts/128349/destination-decision-model-for-cruising-taxis-based-on-embedding-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/128349.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">138</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17869</span> Best Resource Recommendation for a Stochastic Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Likewin%20Thomas">Likewin Thomas</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20V.%20Manoj%20Kumar"> M. V. Manoj Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Annappa"> B. Annappa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this study was to develop an Artificial Neural Network0 s recommendation model for an online process using the complexity of load, performance, and average servicing time of the resources. Here, the proposed model investigates the resource performance using stochastic gradient decent method for learning ranking function. A probabilistic cost function is implemented to identify the optimal θ values (load) on each resource. Based on this result the recommendation of resource suitable for performing the currently executing task is made. The test result of CoSeLoG project is presented with an accuracy of 72.856%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ADALINE" title="ADALINE">ADALINE</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=gradient%20decent" title=" gradient decent"> gradient decent</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20mining" title=" process mining"> process mining</a>, <a href="https://publications.waset.org/abstracts/search?q=resource%20behaviour" title=" resource behaviour"> resource behaviour</a>, <a href="https://publications.waset.org/abstracts/search?q=polynomial%20regression%20model" title=" polynomial regression model"> polynomial regression model</a> </p> <a href="https://publications.waset.org/abstracts/45008/best-resource-recommendation-for-a-stochastic-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45008.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">390</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17868</span> A Context Aware Mobile Learning System with a Cognitive Recommendation Engine</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jalal%20Maqbool">Jalal Maqbool</a>, <a href="https://publications.waset.org/abstracts/search?q=Gyu%20Myoung%20Lee"> Gyu Myoung Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Using smart devices for context aware mobile learning is becoming increasingly popular. This has led to mobile learning technology becoming an indispensable part of today’s learning environment and platforms. However, some fundamental issues remain - namely, mobile learning still lacks the ability to truly understand human reaction and user behaviour. This is due to the fact that current mobile learning systems are passive and not aware of learners’ changing contextual situations. They rely on static information about mobile learners. In addition, current mobile learning platforms lack the capability to incorporate dynamic contextual situations into learners’ preferences. Thus, this thesis aims to address these issues highlighted by designing a context aware framework which is able to sense learner’s contextual situations, handle data dynamically, and which can use contextual information to suggest bespoke learning content according to a learner’s preferences. This is to be underpinned by a robust recommendation system, which has the capability to perform these functions, thus providing learners with a truly context-aware mobile learning experience, delivering learning contents using smart devices and adapting to learning preferences as and when it is required. In addition, part of designing an algorithm for the recommendation engine has to be based on learner and application needs, personal characteristics and circumstances, as well as being able to comprehend human cognitive processes which would enable the technology to interact effectively and deliver mobile learning content which is relevant, according to the learner’s contextual situations. The concept of this proposed project is to provide a new method of smart learning, based on a capable recommendation engine for providing an intuitive mobile learning model based on learner actions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aware" title="aware">aware</a>, <a href="https://publications.waset.org/abstracts/search?q=context" title=" context"> context</a>, <a href="https://publications.waset.org/abstracts/search?q=learning" title=" learning"> learning</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile" title=" mobile"> mobile</a> </p> <a href="https://publications.waset.org/abstracts/60848/a-context-aware-mobile-learning-system-with-a-cognitive-recommendation-engine" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60848.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">245</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17867</span> Social Data-Based Users Profiles' Enrichment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amel%20Hannech">Amel Hannech</a>, <a href="https://publications.waset.org/abstracts/search?q=Mehdi%20Adda"> Mehdi Adda</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamid%20Mcheick"> Hamid Mcheick</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a generic model of user profile integrating several elements that may positively impact the research process. We exploit the classical behavior of users and integrate a delimitation process of their research activities into several research sessions enriched with contextual and temporal information, which allows reflecting the current interests of these users in every period of time and infer data freshness. We argue that the annotation of resources gives more transparency on users' needs. It also strengthens social links among resources and users, and can so increase the scope of the user profile. Based on this idea, we integrate the social tagging practice in order to exploit the social users' behavior to enrich their profiles. These profiles are then integrated into a recommendation system in order to predict the interesting personalized items of users allowing to assist them in their researches and further enrich their profiles. In this recommendation, we provide users new research experiences. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=user%20profiles" title="user profiles">user profiles</a>, <a href="https://publications.waset.org/abstracts/search?q=topical%20ontology" title=" topical ontology"> topical ontology</a>, <a href="https://publications.waset.org/abstracts/search?q=contextual%20%20information" title=" contextual information"> contextual information</a>, <a href="https://publications.waset.org/abstracts/search?q=folksonomies" title=" folksonomies"> folksonomies</a>, <a href="https://publications.waset.org/abstracts/search?q=tags%27%20clusters" title=" tags' clusters"> tags' clusters</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20freshness" title=" data freshness"> data freshness</a>, <a href="https://publications.waset.org/abstracts/search?q=association%20rules" title=" association rules"> association rules</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20recommendation" title=" data recommendation"> data recommendation</a> </p> <a href="https://publications.waset.org/abstracts/56995/social-data-based-users-profiles-enrichment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56995.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">264</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17866</span> Design and Implementation of a Software Platform Based on Artificial Intelligence for Product Recommendation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Giuseppina%20Settanni">Giuseppina Settanni</a>, <a href="https://publications.waset.org/abstracts/search?q=Antonio%20Panarese"> Antonio Panarese</a>, <a href="https://publications.waset.org/abstracts/search?q=Raffaele%20Vaira"> Raffaele Vaira</a>, <a href="https://publications.waset.org/abstracts/search?q=Maurizio%20Galiano"> Maurizio Galiano</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowdays, artificial intelligence is used successfully in academia and industry for its ability to learn from a large amount of data. In particular, in recent years the use of machine learning algorithms in the field of e-commerce has spread worldwide. In this research study, a prototype software platform was designed and implemented in order to suggest to users the most suitable products for their needs. The platform includes a chatbot and a recommender system based on artificial intelligence algorithms that provide suggestions and decision support to the customer. The recommendation systems perform the important function of automatically filtering and personalizing information, thus allowing to manage with the IT overload to which the user is exposed on a daily basis. Recently, international research has experimented with the use of machine learning technologies with the aim to increase the potential of traditional recommendation systems. Specifically, support vector machine algorithms have been implemented combined with natural language processing techniques that allow the user to interact with the system, express their requests and receive suggestions. The interested user can access the web platform on the internet using a computer, tablet or mobile phone, register, provide the necessary information and view the products that the system deems them most appropriate. The platform also integrates a dashboard that allows the use of the various functions, which the platform is equipped with, in an intuitive and simple way. Artificial intelligence algorithms have been implemented and trained on historical data collected from user browsing. Finally, the testing phase allowed to validate the implemented model, which will be further tested by letting customers use it. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title="machine learning">machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=recommender%20system" title=" recommender system"> recommender system</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20platform" title=" software platform"> software platform</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a> </p> <a href="https://publications.waset.org/abstracts/150813/design-and-implementation-of-a-software-platform-based-on-artificial-intelligence-for-product-recommendation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150813.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">134</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17865</span> An Activity Based Trajectory Search Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Mahmoud%20Hasan">Mohamed Mahmoud Hasan</a>, <a href="https://publications.waset.org/abstracts/search?q=Hoda%20M.%20O.%20Mokhtar"> Hoda M. O. Mokhtar </a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the gigantic increment in portable applications use and the spread of positioning and location-aware technologies that we are seeing today, new procedures and methodologies for location-based strategies are required. Location recommendation is one of the highly demanded location-aware applications uniquely with the wide accessibility of social network applications that are location-aware including Facebook check-ins, Foursquare, and others. In this paper, we aim to present a new methodology for location recommendation. The proposed approach coordinates customary spatial traits alongside other essential components including shortest distance, and user interests. We also present another idea namely, <em>"activity trajectory"</em> that represents trajectory that fulfills the set of activities that the user is intrigued to do. The approach dispatched acquaints the related distance value to select trajectory(ies) with minimum cost value (distance) and spatial-area to prune unneeded directions. The proposed calculation utilizes the idea of movement direction to prescribe most comparable N-trajectory(ies) that matches the client's required action design with least voyaging separation. To upgrade the execution of the proposed approach, parallel handling is applied through the employment of a MapReduce based approach. Experiments taking into account genuine information sets were built up and tested for assessing the proposed approach. The exhibited tests indicate how the proposed approach beets different strategies giving better precision and run time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=location%20based%20recommendation" title="location based recommendation">location based recommendation</a>, <a href="https://publications.waset.org/abstracts/search?q=map-reduce" title=" map-reduce"> map-reduce</a>, <a href="https://publications.waset.org/abstracts/search?q=recommendation%20system" title=" recommendation system"> recommendation system</a>, <a href="https://publications.waset.org/abstracts/search?q=trajectory%20search" title=" trajectory search"> trajectory search</a> </p> <a href="https://publications.waset.org/abstracts/56546/an-activity-based-trajectory-search-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56546.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">223</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17864</span> Fairness in Recommendations Ranking: From Pairwise Approach to Listwise Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Patik%20Joslin%20Kenfack">Patik Joslin Kenfack</a>, <a href="https://publications.waset.org/abstracts/search?q=Polyakov%20Vladimir%20Mikhailovich"> Polyakov Vladimir Mikhailovich</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine Learning (ML) systems are trained using human generated data that could be biased by implicitly containing racist, sexist, or discriminating data. ML models learn those biases or even amplify them. Recent research in work on has begun to consider issues of fairness. The concept of fairness is extended to recommendation. A recommender system will be considered fair if it doesn’t under rank items of protected group (gender, race, demographic...). Several metrics for evaluating fairness concerns in recommendation systems have been proposed, which take pairs of items as ‘instances’ in fairness evaluation. It doesn’t take in account the fact that the fairness should be evaluated across a list of items. The paper explores a probabilistic approach that generalize pairwise metric by using a list k (listwise) of items as ‘instances’ in fairness evaluation, parametrized by k. We also explore new regularization method based on this metric to improve fairness ranking during model training. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fairness" title="Fairness">Fairness</a>, <a href="https://publications.waset.org/abstracts/search?q=Recommender%20System" title=" Recommender System"> Recommender System</a>, <a href="https://publications.waset.org/abstracts/search?q=Ranking" title=" Ranking"> Ranking</a>, <a href="https://publications.waset.org/abstracts/search?q=Listwise%20Approach" title=" Listwise Approach"> Listwise Approach</a> </p> <a href="https://publications.waset.org/abstracts/124058/fairness-in-recommendations-ranking-from-pairwise-approach-to-listwise-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124058.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> 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