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

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.

WWW '22: The ACM Web Conference Proceedings

References in corpus (8)

• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Causal Inference in the Presence of Latent Variables and Selection Bias
• Compositional Fairness Constraints for Graph Embeddings
• $F{M}^2$: Field-matrixed Factorization Machines for Recommender Systems
• Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval
• CASTLE: Regularization via Auxiliary Causal Graph Discovery
• The Limits of Popularity-Based Recommendations, and the Role of Social Ties
• Hybrid Model with Time Modeling for Sequential Recommender Systems