[2603.25126] MCLMR: A Model-Agnostic Causal Learning Framework for Multi-Behavior Recommendation

Computer Science > Information Retrieval arXiv:2603.25126 (cs) [Submitted on 26 Mar 2026] Title:MCLMR: A Model-Agnostic Causal Learning Framework for Multi-Behavior Recommendation Authors:Ranxu Zhang, Junjie Meng, Ying Sun, Ziqi Xu, Bing Yin, Hao Li, Yanyong Zhang, Chao Wang View a PDF of the paper titled MCLMR: A Model-Agnostic Causal Learning Framework for Multi-Behavior Recommendation, by Ranxu Zhang and 6 other authors View PDF Abstract: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 can be seamlessly integrated into various MBR architectures. MCLMR first constructs a causal graph to model confounding effects and performs interventions for unbiased preference estimation. Under this causal framework, it employs an Adaptive Aggregation module based on Mixture-of-Experts to dynamically fuse auxiliary behavior information and a Bias-aware...