Social collaborative viewpoint regression with explainable recommendations

Zhaochun Ren, Shangsong Liang, P Li, S Wang, Maarten De Rijke

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

129 Scopus citations

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.

Original languageAmerican English
Title of host publicationSocial collaborative viewpoint regression with explainable recommendations
PublisherAssociation for Computing Machinery, Inc
Pages485-494
Number of pages10
ISBN (Electronic)9781450346757
DOIs
StatePublished - 2017
Event10th ACM International Conference on Web Search and Data Mining, WSDM 2017 - Cambridge, United Kingdom
Duration: Feb 6 2017Feb 10 2017

Publication series

NameWSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining

Conference

Conference10th ACM International Conference on Web Search and Data Mining, WSDM 2017
Country/TerritoryUnited Kingdom
CityCambridge
Period02/6/1702/10/17

Keywords

  • Recommender systems
  • Topic modeling
  • Trusted social relations
  • User comment analysis

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