Cross-Domain Causal Preference Learning for Out-of-Distribution Recommendation

TL;DR

This paper introduces CDCOR, a novel recommendation model that improves out-of-distribution generalization by learning domain-shared preferences and causal invariances, addressing distribution shifts in user data.

Contribution

The paper proposes CDCOR, combining domain adversarial networks and causal structure learning to enhance OOD recommendation performance.

Findings

CDCOR outperforms benchmark models in OOD scenarios.

The model effectively captures domain-shared preferences.

It demonstrates robustness in data sparsity conditions.

Abstract

Recommender systems use users' historical interactions to learn their preferences and deliver personalized recommendations from a vast array of candidate items. Current recommender systems primarily rely on the assumption that the training and testing datasets have identical distributions, which may not hold true in reality. In fact, the distribution shift between training and testing datasets often occurs as a result of the evolution of user attributes, which degrades the performance of the conventional recommender systems because they fail in Out-of-Distribution (OOD) generalization, particularly in situations of data sparsity. This study delves deeply into the challenge of OOD generalization and proposes a novel model called Cross-Domain Causal Preference Learning for Out-of-Distribution Recommendation (CDCOR), which involves employing a domain adversarial network to uncover users' domain-shared preferences and utilizing a causal structure learner to capture causal invariance to deal with the OOD problem. Through extensive experiments on two real-world datasets, we validate the remarkable performance of our model in handling diverse scenarios of data sparsity and out-of-distribution environments. Furthermore, our approach surpasses the benchmark models, showcasing outstanding capabilities in out-of-distribution generalization.