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Open AccessArticle

Cross-Domain Recommendation Based on Sentiment Analysis and Latent Feature Mapping

Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
School of Information Science and Engineering, Central South University, Changsha 410083, China
Author to whom correspondence should be addressed.
Entropy 2020, 22(4), 473;
Received: 25 February 2020 / Revised: 9 April 2020 / Accepted: 17 April 2020 / Published: 20 April 2020
(This article belongs to the Special Issue Computation in Complex Networks)
Cross-domain recommendation is a promising solution in recommendation systems by using relatively rich information from the source domain to improve the recommendation accuracy of the target domain. Most of the existing methods consider the rating information of users in different domains, the label information of users and items and the review information of users on items. However, they do not effectively use the latent sentiment information to find the accurate mapping of latent features in reviews between domains. User reviews usually include user’s subjective views, which can reflect the user’s preferences and sentiment tendencies to various attributes of the items. Therefore, in order to solve the cold-start problem in the recommendation process, this paper proposes a cross-domain recommendation algorithm (CDR-SAFM) based on sentiment analysis and latent feature mapping by combining the sentiment information implicit in user reviews in different domains. Different from previous sentiment research, this paper divides sentiment into three categories based on three-way decision ideas—namely, positive, negative and neutral—by conducting sentiment analysis on user review information. Furthermore, the Latent Dirichlet Allocation (LDA) is used to model the user’s semantic orientation to generate the latent sentiment review features. Moreover, the Multilayer Perceptron (MLP) is used to obtain the cross domain non-linear mapping function to transfer the user’s sentiment review features. Finally, this paper proves the effectiveness of the proposed CDR-SAFM framework by comparing it with existing recommendation algorithms in a cross-domain scenario on the Amazon dataset. View Full-Text
Keywords: cross-domain recommendation; sentiment analysis; latent sentiment review feature; non-linear mapping cross-domain recommendation; sentiment analysis; latent sentiment review feature; non-linear mapping
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Wang, Y.; Yu, H.; Wang, G.; Xie, Y. Cross-Domain Recommendation Based on Sentiment Analysis and Latent Feature Mapping. Entropy 2020, 22, 473.

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