Robust Cross-Domain Sequential Recommendation Driven by Causal Disentanglement and Counterfactual Generation
TL;DR
A novel framework driven by causal disentanglement and counterfactual generation, namely Causal Disentanglement and Counterfactual Generation (CDCG) is proposed, which generates Counterfactual Sequence Generation and Validation (CSGV) to augment training data while guaranteeing the rationality of temporal logic.
Abstract
Sequential recommendation systems are confronted with the dual challenges of data sparsity and domain bias. Especially in cross-domain scenarios, user interests are deeply coupled with domain-specific noise, which severely restricts recommendation performance. Targeted at improving the robustness of cross-domain sequential recommendation, this paper proposes a novel framework driven by causal disentanglement and counterfactual generation, namely Causal Disentanglement and Counterfactual Generation (CDCG). The framework focuses on three key components of sequential recommendation: representation learning, data augmentation and robust training. Firstly, the Cross-Domain Causal Decoupling (CDCD) based on front-door adjustment is adopted to separate genuine user interests from domain biases in an unsupervised manner, so as to provide purified causal representations for cross-domain recommendation. Secondly, guided by structural causal models, the framework generates Counterfactual Sequence Generation and Validation (CSGV) to augment training data while guaranteeing the rationality of temporal logic. Finally, by integrating distributionally robust optimization and mechanism preservation regularization, the Counterfactual Robust Optimization and Mechanism Preservation (CROMP) module enhances the model’s adaptive capability to leverage noisy counterfactual data. Extensive experiments on two benchmark datasets, Amazon and MovieLens, demonstrate that CDCG outperforms the state-of-the-art baselines by 11.1% in extremely sparse settings with only 1% of data available (p < 0.001). In particular, it achieves a 17.5% improvement for ultra cold-start users (1–2 interactions), who constitute 35% of the target population under 1% sparsity, with progressively smaller gains for users with 3–5 interactions (13.8%) and 6–10 interactions (10.1%). The results verify the effectiveness of the causal-driven paradigm in promoting the overall performance of cross-domain sequential recommendation systems.