文献类型：期刊文献

英文题名：Enhancing Collaborative Filtering via Topic Model Integrated Uniform Euclidean Distance

作者：Gao, Tieliang[1,2];Cheng, Bo[1];Chen, Junliang[1];Chen, Ming[3]

第一作者：高铁梁;Gao, Tieliang

通讯作者：Cheng, B[1]

机构：[1]Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China;[2]Xinxiang Univ, Sch Business, Xinxiang 453003, Peoples R China;[3]Zhengzhou Univ Light Ind, Software Engn Coll, Zhengzhou 450000, Henan, Peoples R China

第一机构：Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China

通讯机构：[1]corresponding author), Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China.

年份：2017

卷号：14

期号：11

起止页码：48-58

外文期刊名：CHINA COMMUNICATIONS

收录：;Scopus(收录号：2-s2.0-85040195094);WOS:【SCI-EXPANDED(收录号:WOS:000417915000006)】；

基金：This work was supported in part by the National High - tech R&D Program of China (863 Program) under Grant No. 2013AA102301, and technological project of Henan province (162102210214).

语种：英文

外文关键词：recommendation system; topic model; user interest; uniform euclidean distance

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.