CausPref: Causal Preference Learning for Out-of-Distribution Recommendation

TL;DR

CausPref introduces a causal preference learning framework for recommender systems that enhances out-of-distribution generalization and stability, especially with implicit feedback, by modeling invariant user preferences and anti-preference sampling.

Contribution

The paper presents a novel causal preference-based recommendation framework, CausPref, incorporating a DAG learner and anti-preference sampling to improve OOD robustness in implicit feedback scenarios.

Findings

Outperforms benchmark models under OOD settings

Demonstrates significant improvements in real-world datasets

Offers enhanced interpretability of recommendations

Abstract

In spite of the tremendous development of recommender system owing to the progressive capability of machine learning recently, the current recommender system is still vulnerable to the distribution shift of users and items in realistic scenarios, leading to the sharp decline of performance in testing environments. It is even more severe in many common applications where only the implicit feedback from sparse data is available. Hence, it is crucial to promote the performance stability of recommendation method in different environments. In this work, we first make a thorough analysis of implicit recommendation problem from the viewpoint of out-of-distribution (OOD) generalization. Then under the guidance of our theoretical analysis, we propose to incorporate the recommendation-specific DAG learner into a novel causal preference-based recommendation framework named CausPref, mainly consisting of causal learning of invariant user preference and anti-preference negative sampling to deal with implicit feedback. Extensive experimental results from real-world datasets clearly demonstrate that our approach surpasses the benchmark models significantly under types of out-of-distribution settings, and show its impressive interpretability.