MORA-LLM: Enhancing Multi-Objective Optimization Recommendation Algorithm by Integrating Large Language Models

Multi-objective evolutionary algorithms (MOEAs) have achieved notable success in recommendation systems (RSs) by meeting diverse user needs. However, existing MOEAs lack effective methods to coordinate the challenges of cold start, low convergence of multiple objectives and lack of explainable recommendation reasons. Therefore, we propose an enhancing multi-objective optimization recommendation algorithm by integrating large language models (named as MORA-LLM). MORA-LLM uses the vast knowledge, reasoning ability and natural language generation (NLG) ability of large language models (LLMs) to compensate for the shortcomings of MOEA-based RSs in semantic understanding. Firstly, an LLM-enhancing prediction score (LEPS) strategy is proposed to alleviate the cold start problem. LEPS obtains the user embedding vector by vast knowledge of LLM and extracts interaction information of similar users to improve the accuracy of prediction scores. Secondly, an LLM-enhancing search (LES) strategy is proposed to improve the convergence of the multi-objective. LES strategy combines the reasoning ability of LLM with the competitive idea of competitive swarm optimization to achieve efficient search and balance multiple objectives. Finally, the prediction scores are further corrected based on the LLM output results and MORA-LLM offers recommendation reasons to help users better understand the recommendation results. Experimental results on real-world datasets demonstrate that MORA-LLM significantly outperforms existing algorithms in terms of recommendation accuracy and convergence.