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

TL;DR

This paper introduces MCLMR, a flexible causal learning framework for multi-behavior recommendation systems that effectively models complex confounding factors and aligns heterogeneous behaviors, leading to improved recommendation accuracy.

Contribution

MCLMR is a novel, model-agnostic causal framework that enhances multi-behavior recommendation by modeling confounders, adaptive behavior fusion, and bias-aware representation alignment.

Findings

Significant performance improvements on three real-world datasets.

Effective modeling of confounding effects in multi-behavior data.

General applicability across various baseline models.

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 Contrastive Learning module to align cross-behavior representations in a bias-aware manner. Extensive experiments on three real-world datasets demonstrate that MCLMR achieves significant performance improvements across various baseline models, validating its effectiveness and generality. All data and code will be made publicly available. For anonymous review, our code is available at the following the link: https://github.com/gitrxh/MCLMR.