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Influence of Review Properties in the Usefulness Analysis of Consumer Reviews: A Review-Based Recommender System for Rating Prediction

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Abstract

Most e-commerce sites such as Amazon provide a comment function, and with the rapid growth of the number of comments, selecting and presenting useful comments helps users with decision-making. Recently, recommender systems using reviews instead of rating matrix enhance the recommendation quality by extracting the user preferences and item characteristics from the reviews. Some deep learning methods such as the attention mechanisms are used in these models to judge the review usefulness. However, these approaches rely on the historical data and do not perform well on the unseen reviews. In addition, the existing models ignore the sequential information embedded in the item reviews. In this work, we propose a deep learning model called review-based recommender with attentive properties (RRAP), which combines the review properties and sequential information to mitigate the problems in the traditional recommender systems. We perform experiments to compare the performance of the proposed recommender system with other recommender systems presented in the literature by using Amazon’s four publicly available datasets. We use mean square error as an evaluation metric. The results show that the proposed RRAP reduces the prediction error and improves the interpretability of the model to a certain extent.

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Code availability

The code are available from the corresponding author on reasonable request.

Data Availibility Statement

In this paper, the Amazon dataset has been used (http://jmcauley.ucsd.edu/data/amazon/links.html). The datasets used or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work is supported by the Natural Science Foundation of China (No. 61672337, 61972357), Zhejiang Key R &D Program (No. 2019C03135).

Funding

Natural Science Foundation of China (No. 61672337, 61972357), Zhejiang Key R &D Program (No. 2019C03135).

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Author notes

  1. Jingsheng Lei, Chensicong Zhu and Shengying Yang have contributed equally to this work.

Authors and Affiliations

  1. College of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023, Zhejiang, China

    Jingsheng Lei, Chensicong Zhu, Shengying Yang & Junxia Wang

  2. Zhejiang Dingli Industry Co. Ltd., Lishui, 321400, Zhejiang, China

    Shengying Yang & YunXiang Yu

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  1. Jingsheng Lei
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  2. Chensicong Zhu
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Contributions

JL, CZ and SY designed the study, performed the research, analysed data, and wrote the paper. JW and YY contributed to the writing and revisions. All authors reviewed the manuscript.

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Correspondence to Shengying Yang.

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Lei, J., Zhu, C., Yang, S. et al. Influence of Review Properties in the Usefulness Analysis of Consumer Reviews: A Review-Based Recommender System for Rating Prediction. Neural Process Lett 55, 11035–11054 (2023). https://doi.org/10.1007/s11063-023-11363-5

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