Search results for: Hyunchul Ahn

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class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Hyunchul Ahn"> <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> 5</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Hyunchul Ahn</h1> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5</span> Context-aware Recommender Systems using Data Mining Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Kyoung-jae%20Kim">Kyoung-jae Kim</a>, <a href="https://publications.waset.org/search?q=Hyunchul%20Ahn"> Hyunchul Ahn</a>, <a href="https://publications.waset.org/search?q=Sangwon%20Jeong"> Sangwon Jeong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study proposes a novel recommender system to provide the advertisements of context-aware services. Our proposed model is designed to apply a modified collaborative filtering (CF) algorithm with regard to the several dimensions for the personalization of mobile devices – location, time and the user-s needs type. In particular, we employ a classification rule to understand user-s needs type using a decision tree algorithm. In addition, we collect primary data from the mobile phone users and apply them to the proposed model to validate its effectiveness. Experimental results show that the proposed system makes more accurate and satisfactory advertisements than comparative systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Location-based%20advertisement" title="Location-based advertisement">Location-based advertisement</a>, <a href="https://publications.waset.org/search?q=Recommender%20system" title=" Recommender system"> Recommender system</a>, <a href="https://publications.waset.org/search?q=Collaborative%20filtering" title="Collaborative filtering">Collaborative filtering</a>, <a href="https://publications.waset.org/search?q=User%20needs%20type" title=" User needs type"> User needs type</a>, <a href="https://publications.waset.org/search?q=Mobile%20user." title=" Mobile user."> Mobile user.</a> </p> <a href="https://publications.waset.org/8353/context-aware-recommender-systems-using-data-mining-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/8353/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/8353/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/8353/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/8353/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/8353/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/8353/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/8353/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/8353/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/8353/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/8353/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/8353.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">2174</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4</span> A Recommender System Fusing Collaborative Filtering and User’s Review Mining</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Seulbi%20Choi">Seulbi Choi</a>, <a href="https://publications.waset.org/search?q=Hyunchul%20Ahn"> Hyunchul Ahn</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Collaborative filtering (CF) algorithm has been popularly used for recommender systems in both academic and practical applications. It basically generates recommendation results using users’ numeric ratings. However, the additional use of the information other than user ratings may lead to better accuracy of CF. Considering that a lot of people are likely to share their honest opinion on the items they purchased recently due to the advent of the Web 2.0, user's review can be regarded as the new informative source for identifying user's preference with accuracy. Under this background, this study presents a hybrid recommender system that fuses CF and user's review mining. Our system adopts conventional memory-based CF, but it is designed to use both user’s numeric ratings and his/her text reviews on the items when calculating similarities between users. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Recommender%20system" title="Recommender system">Recommender system</a>, <a href="https://publications.waset.org/search?q=collaborative%20filtering" title=" collaborative filtering"> collaborative filtering</a>, <a href="https://publications.waset.org/search?q=text%20mining" title=" text mining"> text mining</a>, <a href="https://publications.waset.org/search?q=review%20mining." title=" review mining."> review mining.</a> </p> <a href="https://publications.waset.org/10005202/a-recommender-system-fusing-collaborative-filtering-and-users-review-mining" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10005202/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10005202/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10005202/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10005202/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10005202/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10005202/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10005202/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10005202/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10005202/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10005202/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10005202.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">1587</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3</span> Hybrid Recommender Systems using Social Network Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Kyoung-Jae%20Kim">Kyoung-Jae Kim</a>, <a href="https://publications.waset.org/search?q=Hyunchul%20Ahn"> Hyunchul Ahn</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>This study proposes novel hybrid social network analysis and collaborative filtering approach to enhance the performance of recommender systems. The proposed model selects subgroups of users in Internet community through social network analysis (SNA), and then performs clustering analysis using the information about subgroups. Finally, it makes recommendations using cluster-indexing CF based on the clustering results. This study tries to use the cores in subgroups as an initial seed for a conventional clustering algorithm. This model chooses five cores which have the highest value of degree centrality from SNA, and then performs clustering analysis by using the cores as initial centroids (cluster centers). Then, the model amplifies the impact of friends in social network in the process of cluster-indexing CF.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Social%20network%20analysis" title="Social network analysis">Social network analysis</a>, <a href="https://publications.waset.org/search?q=Recommender%20systems" title=" Recommender systems"> Recommender systems</a>, <a href="https://publications.waset.org/search?q=Collaborative%20filtering" title=" Collaborative filtering"> Collaborative filtering</a>, <a href="https://publications.waset.org/search?q=Customer%20relationship%20management" title=" Customer relationship management"> Customer relationship management</a> </p> <a href="https://publications.waset.org/1249/hybrid-recommender-systems-using-social-network-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/1249/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/1249/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/1249/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/1249/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/1249/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/1249/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/1249/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/1249/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/1249/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/1249/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/1249.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">2773</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> Multiclass Support Vector Machines with Simultaneous Multi-Factors Optimization for Corporate Credit Ratings</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Hyunchul%20Ahn">Hyunchul Ahn</a>, <a href="https://publications.waset.org/search?q=William%20X.%20S.%20Wong"> William X. S. Wong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Corporate credit rating prediction is one of the most important topics, which has been studied by researchers in the last decade. Over the last decade, researchers are pushing the limit to enhance the exactness of the corporate credit rating prediction model by applying several data-driven tools including statistical and artificial intelligence methods. Among them, multiclass support vector machine (MSVM) has been widely applied due to its good predictability. However, heuristics, for example, parameters of a kernel function, appropriate feature and instance subset, has become the main reason for the critics on MSVM, as they have dictate the MSVM architectural variables. This study presents a hybrid MSVM model that is intended to optimize all the parameter such as feature selection, instance selection, and kernel parameter. Our model adopts genetic algorithm (GA) to simultaneously optimize multiple heterogeneous design factors of MSVM.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Corporate%20credit%20rating%20prediction" title="Corporate credit rating prediction">Corporate credit rating prediction</a>, <a href="https://publications.waset.org/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/search?q=genetic%20algorithms" title=" genetic algorithms"> genetic algorithms</a>, <a href="https://publications.waset.org/search?q=instance%20selection" title=" instance selection"> instance selection</a>, <a href="https://publications.waset.org/search?q=multiclass%20support%20vector%20machines." title=" multiclass support vector machines."> multiclass support vector machines.</a> </p> <a href="https://publications.waset.org/10004243/multiclass-support-vector-machines-with-simultaneous-multi-factors-optimization-for-corporate-credit-ratings" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10004243/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10004243/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10004243/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10004243/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10004243/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10004243/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10004243/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10004243/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10004243/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10004243/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10004243.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">1411</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Corporate Credit Rating using Multiclass Classification Models with order Information</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Hyunchul%20Ahn">Hyunchul Ahn</a>, <a href="https://publications.waset.org/search?q=Kyoung-Jae%20Kim"> Kyoung-Jae Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has been one of the attractive research topics in the literature. In recent years, multiclass classification models such as artificial neural network (ANN) or multiclass support vector machine (MSVM) have become a very appealing machine learning approaches due to their good performance. However, most of them have only focused on classifying samples into nominal categories, thus the unique characteristic of the credit rating - ordinality - has been seldom considered in their approaches. This study proposes new types of ANN and MSVM classifiers, which are named OMANN and OMSVM respectively. OMANN and OMSVM are designed to extend binary ANN or SVM classifiers by applying ordinal pairwise partitioning (OPP) strategy. These models can handle ordinal multiple classes efficiently and effectively. To validate the usefulness of these two models, we applied them to the real-world bond rating case. We compared the results of our models to those of conventional approaches. The experimental results showed that our proposed models improve classification accuracy in comparison to typical multiclass classification techniques with the reduced computation resource. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Artificial%20neural%20network" title="Artificial neural network">Artificial neural network</a>, <a href="https://publications.waset.org/search?q=Corporate%20credit%20rating" title=" Corporate credit rating"> Corporate credit rating</a>, <a href="https://publications.waset.org/search?q=Support%20vector%20machines" title=" Support vector machines"> Support vector machines</a>, <a href="https://publications.waset.org/search?q=Ordinal%20pairwise%20partitioning" title=" Ordinal pairwise partitioning"> Ordinal pairwise partitioning</a> </p> <a href="https://publications.waset.org/3571/corporate-credit-rating-using-multiclass-classification-models-with-order-information" class="btn btn-primary 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