A model-based approach to user preference discovery in multi-criteria recommender system using genetic programming

Abstract

Multi-criteria recommender systems (MCRSs) provide suggestions to users based on their preferences to various criteria. Incorporation of criteria ratings into recommendation framework can provide quality recommendations to users because these ratings can elicit users' preferences efficiently. However, elicitation of user's overall preference based on criteria ratings is a key issue in MCRS. Even though several aggregation methods for the elicitation of users' overall preference have been investigated in the literature, no method has been shown the superiority under all circumstances. Therefore, we propose a model based approach to user preference discovery in multi-criteria RS using genetic programming (GP). In this work, we suggest three-stage process to generate recommendations to users. First, we learn user preference transformation function to aggregate criteria ratings by using GP, and then we utilize the preference function, so derived, for computing similarities in MCRS. Finally, items are recommended to users. Experimental results on Yahoo! Movies dataset show the superiority of our proposed approach in comparison to other aggregation approaches. © 2022 John Wiley & Sons, Ltd.