用图-语言对齐训练大模型，让推荐更懂语义。
Training Large Recommendation Models via Graph-Language Token Alignment
- 将用户物品图谱与预训练语言模型的词元对齐
- 在三个数据集上显著提升推荐效果
- 适合想融合语义信息做推荐的研究者
推荐系统已成为电商和社交平台帮助用户高效筛选海量信息的关键工具。然而，依赖协同过滤的传统推荐系统难以融入文本中的丰富语义信息。大型语言模型在自然语言处理中表现优异，但直接用于推荐会带来预测结果模糊、扩展性差等问题。本文提出一种新框架——图-语言词元对齐（GLTA），通过将交互图中的用户和物品节点与预训练语言模型的词元对齐，有效利用语言模型的推理能力。进一步引入图-语言逻辑匹配（GLLM）优化词元对齐，实现端到端的物品预测，消除自由文本作为推荐结果时的歧义。在三个基准数据集上的大量实验验证了该方法的有效性，消融实验也证明了各模块贡献。
原文摘要 · Abstract (English)
Recommender systems (RS) have become essential tools for helping users efficiently navigate the overwhelming amount of information on e-commerce and social platforms. However, traditional RS relying on Collaborative Filtering (CF) struggles to integrate the rich semantic information from textual data. Meanwhile, large language models (LLMs) have shown promising results in natural language processing, but directly using LLMs for recommendation introduces challenges, such as ambiguity in generating item predictions and inefficiencies in scalability. In this paper, we propose a novel framework to train Large Recommendation models via Graph-Language Token Alignment. By aligning item and user nodes from the interaction graph with pretrained LLM tokens, GLTA effectively leverages the reasoning abilities of LLMs. Furthermore, we introduce Graph-Language Logits Matching (GLLM) to optimize token alignment for end-to-end item prediction, eliminating ambiguity in the free-form text as recommendation results. Extensive experiments on three benchmark datasets demonstrate the effectiveness of GLTA, with ablation studies validating each component.
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