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Content Provider | ACM Digital Library |
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Author | Ren, Zhaochun Liang, Shangsong Li, Piji de Rijke, Maarten Wang, Shuaiqiang |
Abstract | A recommendation is called explainable if it not only predicts a numerical rating for an item, but also generates explanations for users' preferences. Most existing methods for explainable recommendation apply topic models to analyze user reviews to provide descriptions along with the recommendations they produce. So far, such methods have neglected user opinions and influences from social relations as a source of information for recommendations, even though these are known to improve the rating prediction. In this paper, we propose a latent variable model, called social collaborative viewpoint regression (sCVR), for predicting item ratings based on user opinions and social relations. To this end, we use so-called viewpoints, represented as tuples of a concept, topic, and a sentiment label from both user reviews and trusted social relations. In addition, such viewpoints can be used as explanations. We apply a Gibbs EM sampler to infer posterior distributions of sCVR. Experiments conducted on three large benchmark datasets show the effectiveness of our proposed method for predicting item ratings and for generating explanations. |
Starting Page | 485 |
Ending Page | 494 |
Page Count | 10 |
File Format | |
ISBN | 9781450346757 |
DOI | 10.1145/3018661.3018686 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2017-02-02 |
Publisher Place | New York |
Access Restriction | Subscribed |
Subject Keyword | Topic modeling Trusted social relations Recommender systems User comment analysis |
Content Type | Text |
Resource Type | Article |
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