CausPref: Causal Preference Learning for Out-of-Distribution Recommendation
This addresses the issue of unstable recommendation performance in real-world scenarios for users and platforms, representing a novel method for a known bottleneck.
The paper tackles the problem of performance decline in recommender systems due to distribution shifts in users and items, especially with sparse implicit feedback, by proposing a causal preference learning framework called CausPref, which significantly outperforms benchmark models in out-of-distribution settings.
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.