基于用户方面情感的个性化评论推荐

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.While some existing personalized review recommendation models neglect an user's aspect preferences or the user-product interactions for measuring user similarity.Moreover, those works 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, this paper propose an users' aspect sentiment similarity-based personalized review recommendation model ($A2SPR$), which quantifies review helpfulness and recommends reviews that are customized for each individual.Firstly, the paper analyze users' aspect preferences from reviews and improve user similarity with users' fine-grained sentiment similarity and product relevance.Furthermore, the review helpfulness score is redefined at the aspect level, which indicates the review's reference value for users' purchase decisions. Finally, recommending 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.