A Tensor Factorization on Rating Prediction for Recommendation by Feature Extraction from Reviews

Sun, Yongchao; Fang, Guan-Shen; Kamei, Sayaka

doi:10.1109/candar.2019.00036

Cited by 2 publications

(1 citation statement)

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“…In order to solve this problem, efforts are made to recognize and characterize such opinions into sentiments and topics, to enhance the performance of MF. Existing studies include the applications of the topic model [2,3,19], sentiment analysis [21,17], and their combination [14,22,20].…”

Section: Introductionmentioning

confidence: 99%

Matrix Factorization with Topic and Sentiment Analysis for Rating Prediction

Wang

Fang

Kamei

2021

IJNC

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In today's online services, users' feedback such as numerical rating, textual review, time of purchase, and so on for each item is often encouraged to provide. Managers of online services utilize the feedback to improve the quality of their services, or user experience. For example, many recommender systems predict the items that the users may like and purchase in the future using users' historical ratings. With the increase of user data in the systems, more detailed and interpretable information about item features and user sentiments can be extracted from textual reviews that are relative to ratings. In this paper, we propose a novel topic and sentiment matrix factorization model, which leverages both topic and sentiment drawn from the reviews simultaneously. First, we conduct topic analysis and sentiment analysis of reviews using Latent Dirichlet Allocation (LDA) and lexicon construction technique, respectively. Second, we combine the user consistency, which is calculated from his/her reviews and ratings, and helpful votes from other users of reviews to obtain a reliability measure to weight the ratings. Third, we integrate these three parts into the matrix factorization framework for the prediction of ratings. Our experimental comparison using Amazon datasets indicates that the proposed method significantly improves performance compared to traditional matrix factorization up to 14.12%.

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