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Coupled Topic Model for Collaborative Filtering With User-Generated Content
Jan 03, 2017Author：
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Title: Coupled Topic Model for Collaborative Filtering With User-Generated Content
Authors: Wu, S; Guo, WY; Xu, S; Huang, YZ; Wang, L; Tan, TN
Author Full Names: Wu, Shu; Guo, Weiyu; Xu, Song; Huang, Yongzhen; Wang, Liang; Tan, Tieniu
Source: IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS, 46 (6):908-920; 10.1109/THMS.2016.2586480 DEC 2016
Language: English
Abstract: The user-generated content (UGC) is a type of dyadic information that provides description of the interaction between users and items (such as rating, purchasing, etc.). Most conventional methods incorporate either a user profile or the item description, which cannot well utilize this kind of content information. Some other works jointly consider user ratings and reviews, but they are based on the factorization technique and have difficulty in providing explanations on generated recommendations. In this study, a coupled topicmodel (CoTM) for recommendation with UGCis developed. By combiningUGCand ratings, themethod discussed in this study captures both the content-based preferences and collaborative preferences and, thus, can explain both the user and item latent spaces using the topics discovered from the UGC. The learned topics in CoTM can also serve as proper explanations for the generated recommendations. Experimental results show that the proposed CoTM model yields significant improvements over the compared competitive methods on two typical datasets, that is, MovieLens-10M and Citation-network V1. The topics discovered by CoTM can be used not only to illustrate the topic distributions of users and items, but also to explain the generated user-item recommendations.
ISSN: 2168-2291
eISSN: 2168-2305
IDS Number: ED5AT
Unique ID: WOS:000388864400012