Sequential recommender systems predict items that may interest users by modeling their preferences based on historical interactions. Traditional sequential recommendation methods rely on capturing implicit collaborative filtering signals among items. Recent relation-aware sequential recommendation models have achieved promising performance by explicitly incorporating item relations into the modeling of user historical sequences, where most relations are extracted from knowledge graphs. However, existing methods rely on manually predefined relations and suffer the sparsity issue, limiting the generalization ability in diverse scenarios with varied item relations. In this paper, we propose a novel relation-aware sequential recommendation framework with Latent Relation Discovery (LRD). Different from previous relation-aware models that rely on predefined rules, we propose to leverage the Large Language Model (LLM) to provide new types of relations and connections between items. The motivation is that LLM contains abundant world knowledge, which can be adopted to mine latent relations of items for recommendation. Specifically, inspired by that humans can describe relations between items using natural language, LRD harnesses the LLM that has demonstrated human-like knowledge to obtain language knowledge representations of items. These representations are fed into a latent relation discovery module based on the discrete state variational autoencoder (DVAE). Then the self-supervised relation discovery tasks and recommendation tasks are jointly optimized. Experimental results on multiple public datasets demonstrate our proposed latent relations discovery method can be incorporated with existing relation-aware sequential recommendation models and significantly improve the performance. Further analysis experiments indicate the effectiveness and reliability of the discovered latent relations.

翻译：序列推荐系统通过建模用户历史交互中的偏好来预测可能感兴趣的项目。传统序列推荐方法依赖捕获项目间的隐式协同过滤信号。近年来的关系感知序列推荐模型通过将项目关系显式融入用户历史序列建模中取得显著性能提升，其中大部分关系从知识图谱中提取。然而现有方法依赖人工预定义关系且面临稀疏性问题，限制了其在具有多样化项目关系的场景中的泛化能力。本文提出一种新颖的基于隐式关系发现（Latent Relation Discovery, LRD）的关系感知序列推荐框架。不同于依赖预定义规则的传统关系感知模型，我们利用大语言模型（LLM）提供项目间的新型关系与连接。其动机在于LLM包含丰富的世界知识，可挖掘项目间的隐式关系用于推荐。具体而言，受人类能用自然语言描述项目间关系的启发，LRD利用具备类人知识的LLM获取项目的语言知识表征。这些表征被输入基于离散状态变分自编码器（DVAE）的隐式关系发现模块，随后联合优化自监督关系发现任务与推荐任务。在多个公开数据集上的实验证明，我们提出的隐式关系发现方法可融入现有关系感知序列推荐模型并显著提升性能。进一步的分析实验验证了所发现隐式关系的有效性与可靠性。