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recommendation systems</title> <meta name="description" content="Search results for: recommendation systems"> <meta name="keywords" content="recommendation systems"> <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 systems"> <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> 9711</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: recommendation systems</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9711</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">9710</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">9709</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">9708</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">9707</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">9706</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">9705</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">9704</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">9703</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">9702</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">9701</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">9700</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">9699</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">9698</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">9697</span> Unlocking the Future of Grocery Shopping: Graph Neural Network-Based Cold Start Item Recommendations with Reverse Next Item Period Recommendation (RNPR)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tesfaye%20Fenta%20Boka">Tesfaye Fenta Boka</a>, <a href="https://publications.waset.org/abstracts/search?q=Niu%20Zhendong"> Niu Zhendong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recommender systems play a crucial role in connecting individuals with the items they require, as is particularly evident in the rapid growth of online grocery shopping platforms. These systems predominantly rely on user-centered recommendations, where items are suggested based on individual preferences, garnering considerable attention and adoption. However, our focus lies on the item-centered recommendation task within the grocery shopping context. In the reverse next item period recommendation (RNPR) task, we are presented with a specific item and challenged to identify potential users who are likely to consume it in the upcoming period. Despite the ever-expanding inventory of products on online grocery platforms, the cold start item problem persists, posing a substantial hurdle in delivering personalized and accurate recommendations for new or niche grocery items. To address this challenge, we propose a Graph Neural Network (GNN)-based approach. By capitalizing on the inherent relationships among grocery items and leveraging users' historical interactions, our model aims to provide reliable and context-aware recommendations for cold-start items. This integration of GNN technology holds the promise of enhancing recommendation accuracy and catering to users' individual preferences. This research contributes to the advancement of personalized recommendations in the online grocery shopping domain. By harnessing the potential of GNNs and exploring item-centered recommendation strategies, we aim to improve the overall shopping experience and satisfaction of users on these platforms. <p class="card-text"><strong>Keywords:</strong> <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=cold%20start%20item%20recommendations" title=" cold start item recommendations"> cold start item recommendations</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20grocery%20%20%20shopping%20platforms" title=" online grocery shopping platforms"> online grocery shopping platforms</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20neural%20networks" title=" graph neural networks"> graph neural networks</a> </p> <a href="https://publications.waset.org/abstracts/170977/unlocking-the-future-of-grocery-shopping-graph-neural-network-based-cold-start-item-recommendations-with-reverse-next-item-period-recommendation-rnpr" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170977.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">88</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">9696</span> Scientific Recommender Systems Based on Neural Topic Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Smail%20Boussaadi">Smail Boussaadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hassina%20Aliane"> Hassina Aliane</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the rapid growth of scientific literature, it is becoming increasingly challenging for researchers to keep up with the latest findings in their fields. Academic, professional networks play an essential role in connecting researchers and disseminating knowledge. To improve the user experience within these networks, we need effective article recommendation systems that provide personalized content.Current recommendation systems often rely on collaborative filtering or content-based techniques. However, these methods have limitations, such as the cold start problem and difficulty in capturing semantic relationships between articles. To overcome these challenges, we propose a new approach that combines BERTopic (Bidirectional Encoder Representations from Transformers), a state-of-the-art topic modeling technique, with community detection algorithms in a academic, professional network. Experiences confirm our performance expectations by showing good relevance and objectivity in the results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=scientific%20articles" title="scientific articles">scientific articles</a>, <a href="https://publications.waset.org/abstracts/search?q=community%20detection" title=" community detection"> community detection</a>, <a href="https://publications.waset.org/abstracts/search?q=academic%20social%20network" title=" academic social network"> academic social network</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=neural%20topic%20model" title=" neural topic model"> neural topic model</a> </p> <a href="https://publications.waset.org/abstracts/165792/scientific-recommender-systems-based-on-neural-topic-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165792.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">97</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">9695</span> A Goal-Oriented Social Business Process Management Framework</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Ehson%20Rangiha">Mohammad Ehson Rangiha</a>, <a href="https://publications.waset.org/abstracts/search?q=Bill%20Karakostas"> Bill Karakostas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Social Business Process Management (SBPM) promises to overcome limitations of traditional BPM by allowing flexible process design and enactment through the involvement of users from a social community. This paper proposes a meta-model and architecture for socially driven business process management systems. It discusses the main facets of the architecture such as goal-based role assignment that combines social recommendations with user profile, and process recommendation, through a real example of a charity organization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=business%20process%20management" title="business process management">business process management</a>, <a href="https://publications.waset.org/abstracts/search?q=goal-based%20modelling" title=" goal-based modelling"> goal-based modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20recommendation%20social%20collaboration" title=" process recommendation social collaboration"> process recommendation social collaboration</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20BPM" title=" social BPM"> social BPM</a> </p> <a href="https://publications.waset.org/abstracts/9192/a-goal-oriented-social-business-process-management-framework" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9192.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">494</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">9694</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">9693</span> Point-of-Interest Recommender Systems for Location-Based Social Network Services</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=Yunhwan%20Keon"> Yunhwan Keon</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> Location Based Social Network services (LBSNs) is a new term that combines location based service and social network service (SNS). Unlike traditional SNS, LBSNs emphasizes empirical elements in the user's actual physical location. Point-of-Interest (POI) is the most important factor to implement LBSNs recommendation system. POI information is the most popular spot in the area. In this study, we would like to recommend POI to users in a specific area through recommendation system using collaborative filtering. The process is as follows: first, we will use different data sets based on Seoul and New York to find interesting results on human behavior. Secondly, based on the location-based activity information obtained from the personalized LBSNs, we have devised a new rating that defines the user's preference for the area. Finally, we have developed an automated rating algorithm from massive raw data using distributed systems to reduce advertising costs of LBSNs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=location-based%20social%20network%20services" title="location-based social network services">location-based social network services</a>, <a href="https://publications.waset.org/abstracts/search?q=point-of-interest" title=" point-of-interest"> point-of-interest</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/81920/point-of-interest-recommender-systems-for-location-based-social-network-services" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81920.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">9692</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">9691</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">9690</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"> Downloads <span class="badge badge-light">148</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">9689</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">9688</span> Book Recommendation Using Query Expansion and Information Retrieval Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ritesh%20Kumar">Ritesh Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajendra%20Pamula"> Rajendra Pamula</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present our contribution for book recommendation. In our experiment, we combine the results of Sequential Dependence Model (SDM) and exploitation of book information such as reviews, tags and ratings. This social information is assigned by users. For this, we used CLEF-2016 Social Book Search Track Suggestion task. Finally, our proposed method extensively evaluated on CLEF -2015 Social Book Search datasets, and has better performance (nDCG@10) compared to other state-of-the-art systems. Recently we got the good performance in CLEF-2016. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sequential%20dependence%20model" title="sequential dependence model">sequential dependence model</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20information" title=" social information"> social information</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20book%20search" title=" social book search"> social book search</a>, <a href="https://publications.waset.org/abstracts/search?q=query%20expansion" title=" query expansion"> query expansion</a> </p> <a href="https://publications.waset.org/abstracts/68130/book-recommendation-using-query-expansion-and-information-retrieval-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68130.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">289</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">9687</span> Comparative Analysis of Photovoltaic Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Irtaza%20M.%20Syed">Irtaza M. Syed</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaameran%20Raahemifar"> Kaameran Raahemifar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents comparative analysis of photovoltaic systems (PVS) and proposes practical techniques to improve operational efficiency of the PVS. The best engineering and construction practices for PVS are identified and field oriented recommendation are made. Comparative analysis of central and string inverter based, as well as 600 and 1000 VDC PVS are performed. In addition, direct current (DC) and alternating current (AC) photovoltaic (PV) module based systems are compared. Comparison shows that 1000 V DC String Inverters based PVS is the best choice. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=photovoltaic%20module" title="photovoltaic module">photovoltaic module</a>, <a href="https://publications.waset.org/abstracts/search?q=photovoltaic%20systems" title=" photovoltaic systems"> photovoltaic systems</a>, <a href="https://publications.waset.org/abstracts/search?q=operational%20efficiency%20improvement" title=" operational efficiency improvement"> operational efficiency improvement</a>, <a href="https://publications.waset.org/abstracts/search?q=comparative%20analysis" title=" comparative analysis"> comparative analysis</a> </p> <a href="https://publications.waset.org/abstracts/40123/comparative-analysis-of-photovoltaic-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40123.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">485</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">9686</span> Personalized Social Resource Recommender Systems on Interest-Based Social Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=C.%20L.%20Huang">C. L. Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20J.%20Sia"> J. J. Sia </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The interest-based social networks, also known as social bookmark sharing systems, are useful platforms for people to conveniently read and collect internet resources. These platforms also providing function of social networks, and users can share and explore internet resources from the social networks. Providing personalized internet resources to users is an important issue on these platforms. This study uses two types of relationship on the social networks—following and follower and proposes a collaborative recommender system, consisting of two main steps. First, this study calculates the relationship strength between the target user and the target user's followings and followers to find top-N similar neighbors. Second, from the top-N similar neighbors, the articles (internet resources) that may interest the target user are recommended to the target user. In this system, users can efficiently obtain recent, related and diverse internet resources (knowledge) from the interest-based social network. This study collected the experimental dataset from Diigo, which is a famous bookmark sharing system. The experimental results show that the proposed recommendation model is more accurate than two traditional baseline recommendation models but slightly lower than the cosine model in accuracy. However, in the metrics of the diversity and executing time, our proposed model outperforms the cosine model. <p class="card-text"><strong>Keywords:</strong> <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=social%20networks" title=" social networks"> social networks</a>, <a href="https://publications.waset.org/abstracts/search?q=tagging" title=" tagging"> tagging</a>, <a href="https://publications.waset.org/abstracts/search?q=bookmark%20sharing%20systems" title=" bookmark sharing systems"> bookmark sharing systems</a>, <a href="https://publications.waset.org/abstracts/search?q=collaborative%20recommender%20systems" title=" collaborative recommender systems"> collaborative recommender systems</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20management" title=" knowledge management"> knowledge management</a> </p> <a href="https://publications.waset.org/abstracts/90120/personalized-social-resource-recommender-systems-on-interest-based-social-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90120.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">172</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">9685</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">9684</span> Application of Regularized Low-Rank Matrix Factorization in Personalized Targeting </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kourosh%20Modarresi">Kourosh Modarresi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Netflix problem has brought the topic of “Recommendation Systems” into the mainstream of computer science, mathematics, and statistics. Though much progress has been made, the available algorithms do not obtain satisfactory results. The success of these algorithms is rarely above 5%. This work is based on the belief that the main challenge is to come up with “scalable personalization” models. This paper uses an adaptive regularization of inverse singular value decomposition (SVD) that applies adaptive penalization on the singular vectors. The results show far better matching for recommender systems when compared to the ones from the state of the art models in the industry. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convex%20optimization" title="convex optimization">convex optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=LASSO" title=" LASSO"> LASSO</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</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=singular%20value%20decomposition" title=" singular value decomposition"> singular value decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=low%20rank%20approximation" title=" low rank approximation"> low rank approximation</a> </p> <a href="https://publications.waset.org/abstracts/19547/application-of-regularized-low-rank-matrix-factorization-in-personalized-targeting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19547.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">455</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">9683</span> HBTOnto: An Ontology Model for Analyzing Human Behavior Trajectories</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Heba%20M.%20Wagih">Heba M. Wagih</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> Social Network has recently played a significant role in both scientific and social communities. The growing adoption of social network applications has been a relevant source of information nowadays. Due to its popularity, several research trends are emerged to service the huge volume of users including, Location-Based Social Networks (LBSN), Recommendation Systems, Sentiment Analysis Applications, and many others. LBSNs applications are among the highly demanded applications that do not focus only on analyzing the spatiotemporal positions in a given raw trajectory but also on understanding the semantics behind the dynamics of the moving object. LBSNs are possible means of predicting human mobility based on users social ties as well as their spatial preferences. LBSNs rely on the efficient representation of users’ trajectories. Hence, traditional raw trajectory information is no longer convenient. In our research, we focus on studying human behavior trajectory which is the major pillar in location recommendation systems. In this paper, we propose an ontology design patterns with their underlying description logics to efficiently annotate human behavior trajectories. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20behavior%20trajectory" title="human behavior trajectory">human behavior trajectory</a>, <a href="https://publications.waset.org/abstracts/search?q=location-based%20social%20network" title=" location-based social network"> location-based social network</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology" title=" ontology"> ontology</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20network" title=" social network"> social network</a> </p> <a href="https://publications.waset.org/abstracts/34032/hbtonto-an-ontology-model-for-analyzing-human-behavior-trajectories" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34032.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">452</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">9682</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> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=recommendation%20systems&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=recommendation%20systems&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=recommendation%20systems&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=recommendation%20systems&page=5">5</a></li> <li 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