Multi-objective recommendation system utilizing a multi-population knowledge migration framework

Traditional recommendation systems tend to focus on accuracy and prefer recommending popular items, resulting in non-popular items rarely being exposed to users. However, recommending non-popular items to enhance users’ novelty experience is also crucial. Currently, many researchers are dedicated to multi-objective recommendation studies. Nevertheless, existing multi-objective recommendation algorithms often exhibit poor performance on the hypervolume value (HV) metric and lack effective methods to enhance novelty within evolutionary strategies. In this paper, we propose an innovative multi-objective recommendation algorithm based on a multi-population auxiliary evolution framework, abbreviated as MOEA-MIAE. Within this framework, we design three distinct optimization paths aimed at enhancing the convergence performance of the multi-objective algorithm and improving the hypervolume value metric of results. In addition to adopting the classical genetic algorithm as the main evolutionary population, we specifically introduce two auxiliary evolutionary populations. The first auxiliary population employs an HV-based multi-parent crossover method, while the second focuses on increasing the likelihood of generating highly novel solutions during crossover operations. These three evolutionary populations achieve effective complementarity and integration of their strengths through a mutual migration strategy of solution sets. Experimental results demonstrate that the proposed model exhibits superior performance in balancing accuracy and novelty, outperforming other comparable algorithms.