Hypergraph disentangling and cross-level contrastive learning for recommendation

Hypergraphs have emerged as a critical technique to enhance recommendation performance by directly modeling global and higher-order associations in interaction graphs. However, existing hypergraph methods typically focus on high-order relations, neglecting the role of pairwise relations between nodes, and excessively entangle user and item features during convolution. Aiming to tackle the challenges, we introduce a new Hypergraph Disentangling and Cross-Level Contrastive Learning mechanism, named HDCCL. Specifically, we construct dual-perspective hypergraphs of users/items and disentangle them into three complementary views: clique graphs, star graphs, and dynamic weighted graphs, achieving dynamic multiscale feature fusion from both spatial and spectral perspectives. Additionally, a novel bidirectional attention-based feature decoupling mechanism is designed to differentiate domain-specific unique features from cross-domain commonalities, while explicitly integrating neighborhood aggregation signals from lower-order graphs to effectively enhance the feature entanglement issue. Furthermore, this paper introduces a cross-level contrastive learning module to constrain the semantic consistency of multi-views and enhance the robustness of representations. Comprehensive experiments on publicly accessible datasets demonstrate the superiority of our HDCCL.