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.

中文翻译：

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基于大语言模型的具有潜在关系的顺序推荐

顺序推荐系统通过基于历史交互对用户的偏好进行建模来预测用户可能感兴趣的项目。传统的顺序推荐方法依赖于捕获项目之间的隐式协同过滤信号。最近的关系感知顺序推荐模型通过将项目关系明确地纳入用户历史序列的建模中，取得了良好的性能，其中大多数关系是从知识图谱中提取的。然而，现有的方法依赖于手动预定义的关系，并存在稀疏性问题，限制了具有不同项目关系的不同场景的泛化能力。在本文中，我们提出了一种具有潜在关系发现（LRD）的新型关系感知顺序推荐框架。与以前依赖预定义规则的关系感知模型不同，我们建议利用大语言模型（LLM）来提供项目之间的新型关系和连接。动机是LLM包含丰富的世界知识，可以用来挖掘推荐项目的潜在关系。具体来说，受人类可以使用自然语言描述物品之间关系的启发，LRD利用已证明类人知识的法学硕士来获取物品的语言知识表示。这些表示被输入到基于离散状态变分自动编码器（DVAE）的潜在关系发现模块中。然后联合优化自监督关系发现任务和推荐任务。多个公共数据集上的实验结果表明，我们提出的潜在关系发现方法可以与现有的关系感知顺序推荐模型相结合，并显着提高性能。进一步的分析实验表明所发现的潜在关系的有效性和可靠性。