Enhancing Collaborative Filtering via Topic Model Integrated Uniform Euclidean Distance

查看全文 作　　者：Tieliang [1,2]Gao;Bo [1]Cheng;Junliang [1]Chen;Ming [3]Chen

机构地区：[1]State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications;[2]School of Business, Xinxiang University;[3]Software Engineering College, Zhengzhou University of Light Industry

出　　处：《China Communications》索引2017年第14卷第11期,共11页

基　　金：supported in part by the National High‐tech R&D Program of China (863 Program) under Grant No. 2013AA102301;technological project of Henan province (162102210214) 摘　　要：Recommendation system can greatly alleviate the 'information overload' in the big data era. Existing recommendation methods, however, typically focus on predicting missing rating values via analyzing user-item dualistic relationship, which neglect an important fact that the latent interests of users can influence their rating behaviors. Moreover, traditional recommendation methods easily suffer from the high dimensional problem and cold-start problem. To address these challenges, in this paper, we propose a PBUED(PLSA-Based Uniform Euclidean Distance) scheme, which utilizes topic model and uniform Euclidean distance to recommend the suitable items for users. The solution first employs probabilistic latent semantic analysis(PLSA) to extract users' interests, users with different interests are divided into different subgroups. Then, the uniform Euclidean distance is adopted to compute the users' similarity in the same interest subset; finally, the missing rating values of data are predicted via aggregating similar neighbors' ratings. We evaluate PBUED on two datasets and experimental results show PBUED can lead to better predicting performance and ranking performance than other approaches. 关键词：recommendation system topic model user interest uniform euclidean distance

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Enhancing Collaborative Filtering via Topic Model Integrated Uniform Euclidean Distance

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