面向大语言模型的推荐系统综述

Survey of Recommender Systems for Large Language Models

大型语言模型已成为自然语言处理领域中的有力使用工具,并且在近期也成功吸引了推荐系统领域的广泛关注。这些模型凭借自监督学习在庞大的数据集上进行了深度训练,从而在通用表征学习上取得了显著成果。通过微调、提示调整等高效的迁移技术,它们有望全面提升推荐系统的各项性能。在利用语言模型的力量来优化推荐质量的过程中,关键在于充分利用其高质量的文本特征表征以及广泛的外部知识库,以此为基础构建物品与用户之间的紧密联系。为了全面而深入地理解当前基于大型语言模型的推荐系统,文中将这些模型细致地划分为两大类:用于推荐的判别式大型语言模型和用于推荐的生成式大型语言模型。同时,对于后者,还进一步将其细分为约束生成和自由生成,并对这两种方法的相关研究进行了详尽的总结。此外,还指出了该领域面临的关键挑战,并分享了一些有价值的发现,希望可以为研究人员和从业者提供宝贵的灵感与启示。

Large language models have emerged as highly effective tools in the field of natural language processing(NLP)and have recently garnered considerable attention in the domain of recommendation systems(RS).These models have undergone extensive training on vast datasets through self-supervised learning,achieving remarkable results in learning universal representations.With efficient transfer techniques such as fine-tuning and prompt tuning,they have the potential to significantly enhance various aspects of recommendation system performance.The crux of leveraging language models to optimize recommendation quality lies in fully utilizing their high-quality text feature representations and extensive external knowledge bases to establish strong connections between items and users.To gain a comprehensive and in-depth understanding of current recommendation systems based on large language models,this paper meticulously categorizes these models into two main types:discriminative large language models for recommendation(DLLM4Rec)and generative large language models for recommendation(GLLM4Rec).Furthermore,the latter is subdivided into constrained generation and free generation,and a detailed summary of relevant research on these two approaches is provide.Additionally,this paper identifies key challenges in this field and shares valuable findings,hoping to provide researchers and practitioners with precious inspiration and insights.