Integrating Multiple Experts for Correction Process in Interactive Recommendation Systems

Abstract

User rating is obviously considered to be an important type of feedback information for Interactive Recommendation System (RecSys). The quality and credibility of user ratings will eventually influence the quality of recommendation. However, in the real world, there are usually many inconsistent (e.g., mistakes and missing values) or incorrect user ratings. Therefore, expert-based recommendation framework has been studied to select the most relevant experts regarding a certain item's attribute (or value). This kind of RecSys can i) discover user preference and ii) determine a set of experts based on attributes and values of items. In this paper, we propose a consensual recommendation framework, by integrating multiple experts' ratings, to conduct a correction process which aims at modifying the ratings of other users in order to make the system more effective. Since our work assumes that ratings from experts are assumed to be reliable and correct, we first analyze user profile so as to determine preferences and find out a set of experts. Next, we measure a minimal inconsistency interval (MinIncInt) that might contain incorrect ratings. Finally, we propose solutions to correct incorrect ratings based on ratings from multiple experts. The results show that our solutions can improve both the ratings and the quality of RecSys on the whole.