Adaptive genetic algorithm for user preference discovery in multi-criteria recommender systems

Adaptive genetic algorithm for user preference discovery in multi-criteria recommender systems

Blog Article

A Multi-Criteria Recommender System (MCRS) represents users’ preferences on several factors of products and utilizes these preferences while making product recommendations.In recent studies, MCRS has demonstrated the potential of applying Multi-Criteria Decision Making methods to make effective recommendations in several application domains.However, eliciting actual user preferences is still a major challenge in MCRS since we have many criteria for each product.Therefore, this paper proposes a three-phase adaptive genetic algorithm-based approach to discover user preferences in MCRS.Initially, we build a popularfilm.blog model by assigning weights to multi-criteria features and then learn the preferences on each here criteria during similarity computation among users through a genetic algorithm.

This allows us to know the actual preference of the user on each criteria and find other like-minded users for decision making.Finally, products are recommended after making predictions.The comparative results demonstrate that the proposed genetic algorithm based approach outperforms both multi-criteria and single criteria based recommender systems on the Yahoo! Movies dataset based on various evaluation measures.