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

• URL: http://arxiv.org/abs/2506.16893v1
• Date: Fri, 20 Jun 2025 10:30:39 GMT
• Title: Multi-Objective Recommendation in the Era of Generative AI: A Survey of Recent Progress and Future Prospects
• Authors: Zihan Hong, Yushi Wu, Zhiting Zhao, Shanshan Feng, Jianghong Ma, Jiao Liu, Tianjun Wei,
• Abstract summary: generative AI enables content generation, data synthesis, and personalized experiences.<n>Generative AI helps to address the issue of data sparsity and improving the overall performance of recommendation systems.<n>There remains a lack of comprehensive studies on multi-objective recommendation systems based on generative AI technologies.
• Score: 11.092520571626732
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Anomaly Detection and Generation with Diffusion Models: A Survey [51.61574868316922]
  Anomaly detection (AD) plays a pivotal role across diverse domains, including cybersecurity, finance, healthcare, and industrial manufacturing.<n>Recent advancements in deep learning, specifically diffusion models (DMs), have sparked significant interest.<n>This survey aims to guide researchers and practitioners in leveraging DMs for innovative AD solutions across diverse applications.
  arXiv  Detail & Related papers  (2025-06-11T03:29:18Z)
• A Survey on Knowledge-Oriented Retrieval-Augmented Generation [45.65542434522205]
  Retrieval-Augmented Generation (RAG) has gained significant attention in recent years.<n>RAG combines large-scale retrieval systems with generative models.<n>We discuss the key characteristics of RAG, such as its ability to augment generative models with dynamic external knowledge.
  arXiv  Detail & Related papers  (2025-03-11T01:59:35Z)
• Generative Large Recommendation Models: Emerging Trends in LLMs for Recommendation [85.52251362906418]
  This tutorial explores two primary approaches for integrating large language models (LLMs)<n>It provides a comprehensive overview of generative large recommendation models, including their recent advancements, challenges, and potential research directions.<n>Key topics include data quality, scaling laws, user behavior mining, and efficiency in training and inference.
  arXiv  Detail & Related papers  (2025-02-19T14:48:25Z)
• A Retrieval-Augmented Generation Framework for Academic Literature Navigation in Data Science [2.5398014196797614]
  This paper presents an enhanced Retrieval-Augmented Generation application, an artificial intelligence (AI)-based system designed to assist data scientists in accessing precise and contextually relevant academic resources.<n>The AI-powered application integrates advanced techniques, including the GeneRation Of BIbliographic Data (GROBID) technique for extracting information.<n>A comprehensive evaluation using the Retrieval-Augmented Generation Assessment System (RAGAS) framework demonstrates substantial improvements in key metrics.
  arXiv  Detail & Related papers  (2024-12-19T21:14:54Z)
• A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models [71.25225058845324]
  Large Language Models (LLMs) have demonstrated revolutionary abilities in language understanding and generation. Retrieval-Augmented Generation (RAG) can offer reliable and up-to-date external knowledge. RA-LLMs have emerged to harness external and authoritative knowledge bases, rather than relying on the model's internal knowledge.
  arXiv  Detail & Related papers  (2024-05-10T02:48:45Z)
• Emerging Synergies Between Large Language Models and Machine Learning in Ecommerce Recommendations [19.405233437533713]
  Large language models (LLMs) have superior capabilities in basic tasks of language understanding and generation. We introduce a representative approach to learning user and item representations using LLM as a feature encoder. We then reviewed the latest advances in LLMs techniques for collaborative filtering enhanced recommendation systems.
  arXiv  Detail & Related papers  (2024-03-05T08:31:00Z)
• Embedding in Recommender Systems: A Survey [67.67966158305603]
  A crucial aspect is embedding techniques that covert the high-dimensional discrete features, such as user and item IDs, into low-dimensional continuous vectors. Applying embedding techniques captures complex entity relationships and has spurred substantial research. This survey covers embedding methods like collaborative filtering, self-supervised learning, and graph-based techniques.
  arXiv  Detail & Related papers  (2023-10-28T06:31:06Z)
• Recommender Systems in the Era of Large Language Models (LLMs) [62.0129013439038]
  Large Language Models (LLMs) have revolutionized the fields of Natural Language Processing (NLP) and Artificial Intelligence (AI) We conduct a comprehensive review of LLM-empowered recommender systems from various aspects including Pre-training, Fine-tuning, and Prompting.
  arXiv  Detail & Related papers  (2023-07-05T06:03:40Z)
• A Survey on Large Language Models for Recommendation [77.91673633328148]
  Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) This survey presents a taxonomy that categorizes these models into two major paradigms, respectively Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec)
  arXiv  Detail & Related papers  (2023-05-31T13:51:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.