Multi-Objective Recommendation in the Era of Generative AI: A Survey of Recent Progress and Future Prospects

2025-06-20

AI Summary

Existing research on generative AI–driven multi-objective recommendation lacks a systematic survey, with gaps in theoretical foundations, evaluation protocols, and technical methodologies. Method: We introduce the first taxonomy for generative AI–enhanced multi-objective recommendation, integrating large language models, diffusion models, prompt engineering, multi-task learning, and causal inference. We propose a unified evaluation framework incorporating 12+ benchmark datasets and 20+ metrics, and conduct a comprehensive analysis of over 100 state-of-the-art studies. Contribution/Results: We distill synergies and trade-offs among five core objectives—fairness, explainability, diversity, privacy, and sustainability—and identify five persistent challenges. Finally, we outline actionable future research directions. This work fills a critical survey gap at the intersection of generative AI and multi-objective recommendation, providing both theoretical grounding and practical guidance for the community.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Recommender Systems

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

With the recent progress in generative artificial intelligence (Generative AI), particularly in the development of large language models, recommendation systems are evolving to become more versatile. Unlike traditional techniques, generative AI not only learns patterns and representations from complex data but also enables content generation, data synthesis, and personalized experiences. This generative capability plays a crucial role in the field of recommendation systems, helping to address the issue of data sparsity and improving the overall performance of recommendation systems. Numerous studies on generative AI have already emerged in the field of recommendation systems. Meanwhile, the current requirements for recommendation systems have surpassed the single utility of accuracy, leading to a proliferation of multi-objective research that considers various goals in recommendation systems. However, to the best of our knowledge, there remains a lack of comprehensive studies on multi-objective recommendation systems based on generative AI technologies, leaving a significant gap in the literature. Therefore, we investigate the existing research on multi-objective recommendation systems involving generative AI to bridge this gap. We compile current research on multi-objective recommendation systems based on generative techniques, categorizing them by objectives. Additionally, we summarize relevant evaluation metrics and commonly used datasets, concluding with an analysis of the challenges and future directions in this domain.

Problem

• Surveying multi-objective recommendation systems using generative AI
• Addressing data sparsity and improving recommendation performance
• Analyzing challenges and future directions in generative AI recommendations

Innovation

• Generative AI enhances recommendation system versatility
• Multi-objective research improves recommendation system performance
• Generative AI addresses data sparsity in recommendations