A Survey on Large Language Models for Recommendation

• URL: http://arxiv.org/abs/2305.19860v5
• Date: Tue, 18 Jun 2024 08:07:01 GMT
• Title: A Survey on Large Language Models for Recommendation
• Authors: Likang Wu, Zhi Zheng, Zhaopeng Qiu, Hao Wang, Hongchao Gu, Tingjia Shen, Chuan Qin, Chen Zhu, Hengshu Zhu, Qi Liu, Hui Xiong, Enhong Chen,
• Abstract summary: 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)
• Score: 77.91673633328148
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) and have recently gained significant attention in the domain of Recommendation Systems (RS). These models, trained on massive amounts of data using self-supervised learning, have demonstrated remarkable success in learning universal representations and have the potential to enhance various aspects of recommendation systems by some effective transfer techniques such as fine-tuning and prompt tuning, and so on. The crucial aspect of harnessing the power of language models in enhancing recommendation quality is the utilization of their high-quality representations of textual features and their extensive coverage of external knowledge to establish correlations between items and users. To provide a comprehensive understanding of the existing LLM-based recommendation systems, 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), with the latter being systematically sorted out for the first time. Furthermore, we systematically review and analyze existing LLM-based recommendation systems within each paradigm, providing insights into their methodologies, techniques, and performance. Additionally, we identify key challenges and several valuable findings to provide researchers and practitioners with inspiration. We have also created a GitHub repository to index relevant papers on LLMs for recommendation, https://github.com/WLiK/LLM4Rec.

Related papers

• Generative Large Recommendation Models: Emerging Trends in LLMs for Recommendation [85.52251362906418]
  This tutorial explores two primary approaches for integrating large language models (LLMs) It provides a comprehensive overview of generative large recommendation models, including their recent advancements, challenges, and potential research directions. 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)
• From Selection to Generation: A Survey of LLM-based Active Learning [153.8110509961261]
  Large Language Models (LLMs) have been employed for generating entirely new data instances and providing more cost-effective annotations. This survey aims to serve as an up-to-date resource for researchers and practitioners seeking to gain an intuitive understanding of LLM-based AL techniques.
  arXiv  Detail & Related papers  (2025-02-17T12:58:17Z)
• Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond [41.08716571288641]
  We introduce a novel taxonomy that originates from the intrinsic essence of recommendation. We propose a three-tier structure that more accurately reflects the developmental progression of recommendation systems.
  arXiv  Detail & Related papers  (2024-10-10T08:22:04Z)
• MMREC: LLM Based Multi-Modal Recommender System [2.3113916776957635]
  This paper presents a novel approach to enhancing recommender systems by leveraging Large Language Models (LLMs) and deep learning techniques. The proposed framework aims to improve the accuracy and relevance of recommendations by incorporating multi-modal information processing and by the use of unified latent space representation.
  arXiv  Detail & Related papers  (2024-08-08T04:31:29Z)
• Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review [2.780460221321639]
  The paper underscores the significance of Large Language Models in reshaping recommender systems. LLMs exhibit exceptional proficiency in recommending items, showcasing their adeptness in comprehending intricacies of language. Despite their transformative potential, challenges persist, including sensitivity to input prompts, occasional misinterpretations, and unforeseen recommendations.
  arXiv  Detail & Related papers  (2024-02-11T00:24:17Z)
• Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysis [91.5632751731927]
  Large Language Models such as ChatGPT have showcased remarkable abilities in solving general tasks. We propose a general framework for utilizing LLMs in recommendation tasks, focusing on the capabilities of LLMs as recommenders. We analyze the impact of public availability, tuning strategies, model architecture, parameter scale, and context length on recommendation results.
  arXiv  Detail & Related papers  (2024-01-10T08:28:56Z)
• LLMRec: Benchmarking Large Language Models on Recommendation Task [54.48899723591296]
  The application of Large Language Models (LLMs) in the recommendation domain has not been thoroughly investigated. We benchmark several popular off-the-shelf LLMs on five recommendation tasks, including rating prediction, sequential recommendation, direct recommendation, explanation generation, and review summarization. The benchmark results indicate that LLMs displayed only moderate proficiency in accuracy-based tasks such as sequential and direct recommendation.
  arXiv  Detail & Related papers  (2023-08-23T16:32:54Z)
• 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)

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.