MCLMR: A Model-Agnostic Causal Learning Framework for Multi-Behavior Recommendation
Multi-Behavior Recommendation (MBR) leverages multiple user interaction types (e.g., views, clicks, purchases) to enrich preference modeling and alleviate data sparsity issues in traditional single-behavior approaches. However, existing MBR methods face fundamental challenges: they lack principled frameworks to model complex confounding effects from user behavioral habits and item multi-behavior distributions, struggle with effective aggregation of heterogeneous auxiliary behaviors, and fail to align behavioral representations across semantic gaps while accounting for bias distortions. To address these limitations, we propose MCLMR, a novel model-agnostic causal learning framework that ca...
| Authors | Ranxu Zhang, Junjie Meng, Ying Sun et al. |
|---|---|
| Primary category | cs.IR |
| Published | 26 March 2026 |
| Last updated at source | 6 July 2026 |
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