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Electronic Commerce Research
Erschienen in: Electronic Commerce Research 4/2020

29.09.2018

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

verfasst von: Lihua Sun, Junpeng Guo, Yanlin Zhu

Erschienen in: Electronic Commerce Research | Ausgabe 4/2020

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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.
Vorheriger Artikel How can social commerce be boosted? The impact of consumer behaviors on the information dissemination mechanism in a social commerce network
Nächster Artikel Allocating resources for a restaurant that serves regular and group-buying customers

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Metadaten
Titel
A multi-aspect user-interest model based on sentiment analysis and uncertainty theory for recommender systems
verfasst von
Lihua Sun
Junpeng Guo
Yanlin Zhu
Publikationsdatum
29.09.2018
Verlag
Springer US
Erschienen in
Electronic Commerce Research / Ausgabe 4/2020
Print ISSN: 1389-5753
Elektronische ISSN: 1572-9362
DOI
https://doi.org/10.1007/s10660-018-9319-6

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