Research Area:  Machine Learning
Product reviews play an important role in guiding users purchase decision-making in e-commerce platforms. However, it is challenging for users to find helpful reviews that meet their preferences and experiences among an overwhelming amount of reviews. Some works have been done to recommend helpful reviews to users, either from personalized or non-personalized views. While some existing models recommend similar users reviews for a target user, they either neglect the target users aspect preferences or the user-product interactions for measuring user similarity. Moreover, those models predict review helpfulness at the review-level (a review is taken as a whole); few of them consider the aspect-level. To address the above issues, we propose an aspect sentiment similarity-based personalized review recommendation model (A2SPR), which quantifies review helpfulness and recommends reviews that are customized for each individual. We analyze users aspect preferences from reviews and improve user similarity with users fine-grained sentiment and product relevance. Furthermore, we redefine the review helpfulness score at the aspect level, which indicates the reviews reference value for users purchase decisions. Finally, we recommend the top k helpful reviews for individuals based on the review helpfulness score. To validate the performance of the proposed model, eight baselines are developed and compared. Experimental results show that our model performs better than those baselines in both the coverage and precision.
Author(s) Name:  Chunli Huang, Wenjun Jiang, Jie Wu, Guojun Wang
Journal name:  ACM Transactions on Internet Technology
Publisher name:  ACM Digital Library
Volume Information:  Volume 20, Issue 4
Paper Link:   https://dl.acm.org/doi/abs/10.1145/3414841