For modern recommender systems, the use of low-dimensional latent representations to embed users and items based on their observed interactions has become commonplace. However, many existing recommendation models are primarily designed for coarse-grained and homogeneous interactions, which limits their effectiveness in two critical dimensions. Firstly, these models fail to leverage the relational dependencies that exist across different types of user behaviors, such as page views, collects, comments, and purchases. Secondly, they struggle to capture the fine-grained latent factors that drive user interaction patterns. To address these limitations, we present a heterogeneous graph collaborative filtering model MixRec that excels at disentangling users' multi-behavior interaction patterns and uncovering the latent intent factors behind each behavior. Our model achieves this by incorporating intent disentanglement and multi-behavior modeling, facilitated by a parameterized heterogeneous hypergraph architecture. Furthermore, we introduce a novel contrastive learning paradigm that adaptively explores the advantages of self-supervised data augmentation, thereby enhancing the model's resilience against data sparsity and expressiveness with relation heterogeneity. To validate the efficacy of MixRec, we conducted extensive experiments on three public datasets. The results clearly demonstrate its superior performance, significantly outperforming various state-of-the-art baselines. Our model is open-sourced and available at: https://github.com/HKUDS/MixRec.

翻译：在现代推荐系统中，基于观测到的交互行为，使用低维潜在表示来嵌入用户和物品已成为普遍做法。然而，许多现有的推荐模型主要针对粗粒度且同质的交互设计，这限制了它们在两个关键维度上的有效性。首先，这些模型未能利用跨不同用户行为类型（如页面浏览、收藏、评论和购买）存在的关系依赖性。其次，它们难以捕捉驱动用户交互模式的细粒度潜在因素。为解决这些局限性，我们提出了一种异构图协同过滤模型MixRec，该模型擅长解耦用户的多行为交互模式，并揭示每种行为背后的潜在意图因素。我们的模型通过结合意图解耦和多行为建模来实现这一目标，并借助参数化的异构超图架构进行支持。此外，我们引入了一种新颖的对比学习范式，该范式自适应地探索自监督数据增强的优势，从而增强了模型对数据稀疏性的鲁棒性以及关系异构性的表达能力。为验证MixRec的有效性，我们在三个公共数据集上进行了广泛的实验。结果清楚地证明了其卓越的性能，显著优于各种最先进的基线模型。我们的模型已开源，可在以下网址获取：https://github.com/HKUDS/MixRec。