Preference-based user rating correction process for interactive recommendation systems

Abstract

In most of the recommendation systems, user rating is an important user activity that reflects their opinions. Once the users return their ratings about items the systems have suggested, the user ratings can be used to adjust the recommendation process.However, while rating the items users can make some mistakes (e.g., natural noises). As the recommendation systems receive more incorrect ratings, the performance of such systems may decrease. In this paper, we focus on an interactive recommendation system which can help users to correct their own ratings. Thereby, we propose a method to determine whether the ratings from users are consistent to their own preferences (represented as a set of dominant attribute values) or not and eventually to correct these ratings to improve recommendation. The proposed interactive recommendation system has been particularly applied to two user rating datasets (e.g., MovieLens and Netflix) and it has shown better recommendation performance (i.e., lower error ratings).