Review websites allow users to share their reviews of products or businesses, give ratings to products or businesses, and interact with other users. Due to the rapid growth of online review data, users face the problem of information overload. To resolve this problem, many researches have proposed various recommendation methods based on the analysis of users’ ratings. Besides user ratings, the review websites contain unstructured textual data and information of different aspects which has different impact and importance to both users and businesses. It may lead to inaccurate rating predictions, as it is difficult to know users’ preferring aspects and their corresponding importance by analyzing users’ rating data.

To resolve the above-stated problems, this research proposed aspect-based rating prediction methods, i.e., ARPM and ARPM-Social, which integrate aspect detection and sentiment analysis to generate user preference and business performance, combined with the results of social behavior analysis to predict the ratings of the businesses that users will be interested in the future. Based on the experimental results, the proposed methods perform better than other traditional rating-based prediction methods. They can effectively analyze various aspects and sentiments from the reviews of users and businesses, as well as improve the accuracy of rating predictions.

评论网站使用户可以分享他们对产品或企业的评论，对产品或企业进行评级，并与其他用户互动。由于在线评论数据的快速增长，用户面临信息过载的问题。为了解决该问题，许多研究基于对用户评级的分析提出了各种推荐方法。除了用户评分，评论网站还包含非结构化的文本数据和不同方面的信息，这些信息对用户和企业都有不同的影响和重要性。由于难以通过分析用户的评分数据来了解用户的偏爱方面及其相应的重要性，因此可能导致评分预测不准确。

为了解决上述问题，本研究提出了基于方面的评级预测方法，即ARPM和ARPM-Social，它结合了方面检测和情感分析以生成用户偏好和业务绩效，并结合社会行为分析的结果，预测用户将来会感兴趣的业务的评级。根据实验结果，提出的方法比其他传统的基于等级的预测方法表现更好。他们可以从用户和企业的评论中有效地分析各个方面和观点，并提高评级预测的准确性。