用联合表示学习提升大模型推荐的召回效果
RALLRec: Improving Retrieval Augmented Large Language Model Recommendation with Representation Learning
- 融合文本与协同信号，增强物品表征
- 在三个真实数据集上显著优于基线方法
- 适合关注推荐系统与大模型结合的研究者
大型语言模型（LLM）被引入推荐系统以增强对用户行为的理解。检索增强生成（RAG）技术进一步被集成，以检索更相关的物品并提升系统性能。然而，现有RAG方法主要依赖文本语义，常无法捕捉最相关物品，限制了系统效果。本文提出表示学习增强的检索增强大模型推荐方法（RALLRec）。具体而言，通过提示LLM生成更详细的物品描述以增强文本语义，并联合学习由LLM提取的文本表示与推荐模型提取的协同表示。考虑到用户兴趣可能具有时变特性，引入一种简单而有效的重排序方法以捕捉偏好动态变化。我们在三个真实世界数据集上进行了广泛实验，评估结果验证了该方法的有效性。代码已公开于 https://github.com/JianXu95/RALLRec。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have been integrated into recommendation systems to enhance user behavior comprehension. The Retrieval Augmented Generation (RAG) technique is further incorporated into these systems to retrieve more relevant items and improve system performance. However, existing RAG methods rely primarily on textual semantics and often fail to incorporate the most relevant items, limiting the effectiveness of the systems. In this paper, we propose Representation learning for retrieval-Augmented Large Language model Recommendation (RALLRec). Specifically, we enhance textual semantics by prompting LLMs to generate more detailed item descriptions, followed by joint representation learning of textual and collaborative semantics, which are extracted by the LLM and recommendation models, respectively. Considering the potential time-varying characteristics of user interest, a simple yet effective reranking method is further introduced to capture the dynamics of user preference. We conducted extensive experiments on three real-world datasets, and the evaluation results validated the effectiveness of our method. Code is made public at https://github.com/JianXu95/RALLRec.
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