A Novel Recommendation Algorithm Based on Multi-objective Evolution

Abstract

During the past two decades, recommender systems have been playing an increasingly significant role in business decisions for providing personalized recommendations. However, the majority of the present algorithms attach excess importance to accuracy while overlooking users demand for diverse items. We have proposed a novel multi-objective evolutionary recommendation algorithm, which has the capability to look for compromise among accuracy and diversity. Moreover, due to the fact that Pareto optimal solutions of recommendation tend to be sparse, the proposed algorithm implements a new strategy of population initialization with the consideration of the sparse nature of the Pareto solutions. The proposed algorithm will take effect in the recommender systems.