Journal
TSINGHUA SCIENCE AND TECHNOLOGY
Volume 20, Issue 6, Pages 634-643Publisher
TSINGHUA UNIV PRESS
DOI: 10.1109/TST.2015.7350016
Keywords
collaborative filtering; topic model; recommender system; matrix factorization
Categories
Funding
- National Key Basic Research and Development (973) Program of China [2013CB329600]
- National Natural Science Foundation of China [61472040, 60873237]
- Beijing Higher Education Young Elite Teacher Project [YETP1198]
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Intuitively, not only do ratings include abundant information for learning user preferences, but also reviews accompanied by ratings. However, most existing recommender systems take rating scores for granted and discard the wealth of information in accompanying reviews. In this paper, in order to exploit user profiles' information embedded in both ratings and reviews exhaustively, we propose a Bayesian model that links a traditional Collaborative Filtering (CF) technique with a topic model seamlessly. By employing a topic model with the review text and aligning user review topics with user attitudes (i.e., abstract rating patterns) over the same distribution, our method achieves greater accuracy than the traditional approach on the rating prediction task. Moreover, with review text information involved, latent user rating attitudes are interpretable and cold-start problem can be alleviated. This property qualifies our method for serving as a recommender task with very sparse datasets. Furthermore, unlike most related works, we treat each review as a document, not all reviews of each user or item together as one document, to fully exploit the reviews' information. Experimental results on 25 real-world datasets demonstrate the superiority of our model over state-of-the-art methods.
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