| Summary: | 碩士 === 國立交通大學 === 資訊管理研究所 === 105 === Online review website is a popular experience-sharing platform that provides users with reference items and helps users to choose products. However, due to the information overload problem, it is not easy for users to find items that meet their preferences. Moreover, the traditional recommendation approaches usually consider users ratings and ignore the review contents. Those recommended results performed by the traditional approaches could not fit for users’ real preferences. Therefore, it is important to analyze the review contents and ratings of reviews to predict the user's preference rating and improve the recommended quality as an important research topic.
This study proposes a new rating prediction method based on user preferences and contextual information. The method takes into account the different rating factors which include the users’ preferences on different aspects and the contexts of the reviews. In this study, we use text mining as a basis to analyze user preferences, and add users and items contextual information into prediction models to improve the prediction results. The results show that the method proposed in this study outperforms the traditional methods and improves the accuracy of rating prediction.
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