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MAN: Main-auxiliary network with attentive interactions for review-based recommendation

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Abstract

Recently, more and more attention has been paid to the recommender systems incorporating review information. However, there are two main problems. (1) Among the many reviews written by a user, most of the existing works have not considered the special importance of the user’s review for the target item (RT) in building user preferences, which may fail to capture more accurate preferences of the user. (2) Most of the existing work does not dynamically construct the user and the item feature representations in a fine-grained manner according to the aspect characteristics of the target item before user and item nonlinear interaction, which may lead to suboptimal recommendation performance. Therefore, we propose a m ain-a uxiliary n etwork (MAN) based on deep learning for item recommendation. Specifically, MAN uses the auxiliary network to focus on the purification of RT at the word level and assists the main network in generating the predicted value of RT. The main network deals with the user-item interaction according to the relationship between the user multiaspect features and the item as the most prominent aspect feature and then generates the final rating prediction. Note, MAN only uses the main network for testing. Extensive experiments on five public datasets show that MAN outperforms the state-of-the-art methods.

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Data Availability

Dataset is publicly available at: http://jmcauley.ucsd.edu/data-/amazon/links.html

Notes

  1. Available at : http://jmcauley.ucsd.edu/data/amazon/links.html

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Funding

This work is supported by Tianjin “Project + Team” Key Training Project under Grant No. XC202022.

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Authors and Affiliations

  1. Engineering Research Center of Learning-Based Intelligent System, Ministry of Education, Tianjin University of Technology, Tianjin, China

    Peilin Yang, Yingyuan Xiao, Wenguang Zheng, Ke Zhu, Chenchen Sun & Li Liu

  2. Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, China

    Peilin Yang, Yingyuan Xiao, Wenguang Zheng, Ke Zhu, Chenchen Sun & Li Liu

  3. College of General Education, Tianjin Foreign Studies University, Tianjin, China

    Xu Jiao

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  1. Peilin Yang
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  2. Yingyuan Xiao
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Correspondence to Yingyuan Xiao.

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Yang, P., Xiao, Y., Zheng, W. et al. MAN: Main-auxiliary network with attentive interactions for review-based recommendation. Appl Intell 53, 12955–12970 (2023). https://doi.org/10.1007/s10489-022-04135-6

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