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dc.contributor.author連瑞雲en_US
dc.contributor.authorrita sardjonoen_US
dc.contributor.author劉敦仁en_US
dc.contributor.authorDr. Duen-Ren Liuen_US
dc.date.accessioned2014-12-12T01:17:19Z-
dc.date.available2014-12-12T01:17:19Z-
dc.date.issued2003en_US
dc.identifier.urihttp://140.113.39.130/cdrfb3/record/nctu/#GT009034536en_US
dc.identifier.urihttp://hdl.handle.net/11536/38968-
dc.description.abstractRecommender systems have become dominant to reduce information overload and customize information access. The most successful recommender systems is collaborative filtering, which considers preferences of other users sharing similar interests. A major problem of collaborative filtering is the sparsity problem, which refers to a situation in which transactional data is sparse and insufficient to identify similarities in user interests. Accordingly, this research proposed two second-degree neighborhood methods to alleviate the sparsity problem in collaborative filtering. Extensive experiments using EachMovie data is used to analyze the characteristics of our methods. The results show that our approach contributes to the improvement of prediction quality of recommendations, especially the sparsity problem.zh_TW
dc.description.abstractRecommender systems have become dominant to reduce information overload and customize information access. The most successful recommender systems is collaborative filtering, which considers preferences of other users sharing similar interests. A major problem of collaborative filtering is the sparsity problem, which refers to a situation in which transactional data is sparse and insufficient to identify similarities in user interests. Accordingly, this research proposed two second-degree neighborhood methods to alleviate the sparsity problem in collaborative filtering. Extensive experiments using EachMovie data is used to analyze the characteristics of our methods. The results show that our approach contributes to the improvement of prediction quality of recommendations, especially the sparsity problem.en_US
dc.language.isoen_USen_US
dc.subject推薦zh_TW
dc.subject稀疏問題zh_TW
dc.subjectcollaborative filteringen_US
dc.subjectrecommender systemsen_US
dc.subjectinformation loadingen_US
dc.title運用二階層同好群組解決合作式過濾推薦之稀疏問題zh_TW
dc.titleApplying Second-Degree Neighborhood to Alleviate the Sparsity Problem in Collaborative Filteringen_US
dc.typeThesisen_US
dc.contributor.department資訊管理研究所zh_TW
Appears in Collections:Thesis