Preference-based user rate correction process for interactive recommendation systems

Abstract

In recommendation systems, rating is an important user activity reflecting their opinions. Once the users return their rates about the items suggested from the systems, the user rates can be used to adjust recommendation process. However, users can make some mistakes (e.g., nature noises) during rating the items. As the recommendation systems receive more incorrect rates, the performance of such systems might be decreased. To solve the problem, in this paper, we focus on an interactive recommendation system which can help users to correct their own rates.