Integrating Multiple Experts for Correction Process in Interactive Recommendation Systems

Abstract

To improve the performance of the recommendation process, most of recommendation systems (RecSys) should collect better ratings from users. Particularly, rating process is an important task in interactive RecSys which can ask users to correct their own ratings. However, in real world, there are many inconsistencies (e.g., mistakes and missing values) or incorrect in the user ratings. Thereby, expert-based recommendation framework has been studied to select the most relevant experts in a certain item attribute (or value). This kind of RecSys can i) discover user preference and ii) determine a set of experts based on attribute and value of items. In this paper, we propose a consensual recommendation framework integrating multiple experts to conduct correction process. Since the ratings from experts are assumed to be reliable and correct, we first analyze user profile to determine the preference 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 the incorrect rating based on ratings from multiple experts.