An aggregation approach to multi-criteria recommender system using genetic programming

Recommender system is one of the emerging personalization tools in e-commerce domains for suggesting suitable items to users. Traditional collaborative filtering (CF) based recommender systems (RSs) suggest items to users based on the overall ratings to find out similar users. Multi-criteria ratings are used to capture user preferences efficiently in multi-criteria recommender systems (MCRSs), and incorporation of criteria ratings can lead to higher performance in MCRS. However, aggregation of these criteria ratings is a major concern in MCRS. In this paper, we propose a multi-criteria collaborative filtering-based RS by leveraging information derived from multi-criteria ratings through Genetic programming (GP). The proposed system consists of two parts: (1) weights of each user for every criterion are computed through our proposed modified sub-tree crossover in GP process (2) criteria weights are then incorporated in CF process to generate effective recommendations in our proposed system. The obtained results present significant improvements in prediction and recommendation qualities in comparison to heuristic approaches.