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Intuitively, not only do ratings include abundant information for learning user preferences, but also reviews ccompanied 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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A Bayesian Recommender Model for User Rating and Review Profiling

Show Author's information Mingming JiangDandan Song( )Lejian LiaoFeida Zhu
School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China.
School of Information Systems, Singapore Management University, Singapore 178902.

Abstract

Intuitively, not only do ratings include abundant information for learning user preferences, but also reviews ccompanied 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.

Keywords: collaborative filtering, topic model, recommender system, matrix factorization

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Publication history

Received: 07 October 2015
Accepted: 05 November 2015
Published: 17 December 2015
Issue date: December 2015

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© The author(s) 2015

Acknowledgements

This work was supported by the National Key Basic Research and Development (973) Program of China (No. 2013CB329600), the National Natural Science Foundation of China (Nos. 61472040 and 60873237), and Beijing Higher Education Young Elite Teacher Project (No. YETP1198).

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