Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 135, Issue -, Pages 249-258Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2019.06.001
Keywords
Collaborative filtering; Matrix factorization; Recommender system; Sentiment analysis
Categories
Funding
- National Natural Science Foundation of China [41604114]
- Natural Science Foundation of Sichuan Province [2019YJ0314]
- Scientific Innovation Group for Youths of Sichuan Province [2019JDTD0017]
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Recommender systems aim at predicting users' preferences based on abundant information, such as user ratings, demographics, and reviews. Although reviews are sparser than ratings, they provide more detailed and reliable information about users' true preferences. Currently, reviews are often used to improve the explainability of recommender systems. In this paper, we propose the sentiment based matrix factorization with reliability (SBMF+R) algorithm to leverage reviews for prediction. First, we develop a sentiment analysis approach using a new star-based dictionary construction technique to obtain the sentiment score. Second, we design a user reliability measure that combines user consistency and the feedback on reviews. Third, we incorporate the ratings, reviews, and feedback into a probabilistic matrix factorization framework for prediction. Experiments on eight Amazon datasets demonstrated that SBMF+R is more accurate than state-of-the-art algorithms. (C) 2019 Elsevier Ltd. All rights reserved.
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