A Multi-Population Genetic Algorithm for Multiobjective Recommendation System

Nowadays, recommendation systems (RSs) have been widely used in many real-world applications. However, traditional recommendation techniques mainly aim at improving recommendation accuracy, while other metrics to measure the performance of the RSs are not considered. In this paper, a multiobjective recommendation model that considers different metrics, including accuracy, diversity, and novelty of recommendations is established. Compared with recommendation models that only consider accuracy, this model can recommend more different items with higher diversity and more fresh items with higher novelty to enhance the long-term performance of RSs. Moreover, to efficiently solve this multiobjective recommendation model, a multi-population genetic algorithm (MPGA), which follows the multiple populations for multiple objectives (MPMO) framework, is proposed. As far as we know, it is the first time that the advanced MPMO framework is used in RSs. We conduct comparison experiments on three real-world datasets with three state-of-the-art multiobjective recommendation algorithms and two traditional multiobjective evolutionary algorithms. The experimental results indicate that the performance of MPGA is better than all the compared methods.