A multi-aspect user-interest model based on sentiment analysis and uncertainty theory for recommender systems

Lihua Sun, Junpeng Guo () and Yanlin Zhu
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Lihua Sun: Tianjin University
Junpeng Guo: Tianjin University
Yanlin Zhu: Tianjin University

Electronic Commerce Research, 2020, vol. 20, issue 4, No 8, 857-882

Abstract: Abstract This work presents a new multi-aspect user-interest model for recommender systems to improve recommendation and prediction accuracy. We introduce the overall user satisfaction for a product to build a user-interest profile by computing the user-interest levels from multi-aspect reviews. A domain emotional dictionary is built to overcome the gap in quantity between negative and positive words for sentiment polarity analysis. A sentiment analysis model is designed to characterize the users’ sentiment polarity and strength based on uncertainty theory and the domain emotional dictionary. Accordingly, a new multi-aspect user-interest model is proposed by considering the sentiment analysis model with the user-interest profile. Then, our proposed model is applied to recommender systems and experimentally tested on five products of different categories from three e-commerce websites. Our model not only outperforms the traditional and state-of-the-art models on rating prediction tasks but also improves the recommendation accuracy in multiple domains.

Keywords: Recommender system; Sentiment analysis; Uncertainty theory; Product reviews; User interest (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10660-018-9319-6

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