Disentangled Heterogeneous Collaborative Filtering

Lianghao Xia, Meiyan Xie, Long Xia, Yong Xu, Dawei Yin, Chao Huang

Primary Area: learning on graphs and other geometries & topologies

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Keywords: Collaborative Filtering, Recommender System, Contrastive Learning

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Abstract: Modern recommender systems often utilize low-dimensional latent representations to embed users and items based on their observed interactions. However, many existing recommendation models are primarily designed for coarse-grained and homogeneous interactions, which limits their effectiveness in two key dimensions: i) They fail to exploit the relational dependencies across different types of user behaviors, such as page views, add-to-favorites, and purchases. ii) They struggle to encode the fine-grained latent factors that drive user interaction patterns. In this study, we introduce DHCF, an efficient and effective contrastive learning recommendation model that effectively disentangles users' multi-behavior interaction patterns and the latent intent factors behind each behavior. Our model achieves this through the integration of intent disentanglement and multi-behavior modeling using a parameterized heterogeneous hypergraph architecture. Additionally, we propose a novel contrastive learning paradigm that adaptively explores the benefits of multi-behavior contrastive self-supervised augmentation, thereby improving the model's robustness against data sparsity. Through extensive experiments conducted on three public datasets, we demonstrate the effectiveness of DHCF, which significantly outperforms various strong baselines with competitive efficiency.

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Submission Number: 8872