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 user’s 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 review’s 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.

中文翻译：

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基于用户方面情感的个性化评论推荐

产品评论在指导用户在电商平台的购买决策方面发挥着重要作用。然而，用户很难在海量评论中找到符合他们偏好和体验的有用评论。已经完成了一些工作来向用户推荐有用的评论，无论是来自个性化的还是非个性化的视图。虽然一些现有模型为目标用户推荐相似用户的评论，但它们要么忽略目标用户的方面偏好，要么忽略用户-产品交互来衡量用户相似度。此外，这些模型在评论级别预测评论的有用性（评论被视为一个整体）；他们中很少有人考虑方面级别。针对上述问题，我们提出了一种基于方面情感相似度的个性化评论推荐模型（A2SPR)，它量化了评论的有用性并推荐了为每个人定制的评论。我们从评论中分析用户的方面偏好，并通过用户的细粒度情感和产品相关性来提高用户相似度。此外，我们在方面层面重新定义了评论的有用性分数，这表明了评论对用户购买决策的参考价值。最后，我们推荐顶部ķ基于评论有用性分数的个人有用评论。为了验证所提出模型的性能，开发并比较了八个基线。实验结果表明，我们的模型在覆盖率和精度上都优于那些基线。