Evolutionary algorithm with cross-diversity integration and mutation synergy operation for multi-objective recommendation

Abstract

Recommendation algorithms have become increasingly prevalent in modern society, addressing overload by delivering content aligned with user preferences. While accuracy is prioritized in traditional approaches, diversity is also crucial in recommendation systems. However, the balance between these two objectives is challenged by a fundamental trade-off. To address this issue, an enhanced multi-objective evolutionary algorithm (MOEA-EMRS) is proposed, in which cross-diversity mechanism and mutation synergy operation are integrated for multi-objective recommendations. MOEA-EMRS integrates three core components: a novel population initialization mechanism that constructs a distinctive primitive population with enhanced diversity and accuracy, a diversity-preserving crossover operator, and objective-oriented mutation operation specifically designed to reinforce Pareto optimality. To evaluate the algorithm’s performance, comparative experiments were conducted between MOEA-EMRS and existing multi-objective models. Experimental results demonstrate that MOEA-EMRS outperforms existing algorithms in performance effectiveness.