Research on Rating Error and Quality Metrics for Collaborative Filtering Recommendation Methods

The collaborative filtering recommendation system has been widely used in E-commerce as a relatively successful recommendation system. At present, the focus of collaborative filtering recommendation research is mainly on how to improve the accuracy of recommendation by improving the recommendation algorithm. However, in real world, the user’s rating behaviour is not perfectly rational. It is no odd that there is deviation of rating to any a given item for a user in real evaluation. In this case, what it means for the improvement of collaborative filtering recommendation methods, and how they performed when we use the commonly used quality metrics to evaluate the collaborative filtering recommendation methods? In view of these problems, this paper presupposes that the user’s rating behaviour is a bounded rational behaviour, and based on the normality hypothesis of rating error, introduces a simulated rating experiment innovatively to analyse the effect that rating prediction can achieve in sense of the commonly used quality metrics. This study is of significance for the research and application of collaborative filtering recommendation technology.