Robust Collaborative Filtering to Popularity Distribution Shift

Abstract

In leading collaborative filtering (CF) models, representations of users and items are prone to learn popularity bias in the training data as shortcuts. The popularity shortcut tricks are good for in-distribution (ID) performance but poorly generalized to out-of-distribution (OOD) data, i.e., when popularity distribution of test data shifts w.r.t. the training one. To close the gap, debiasing strategies try to assess the shortcut degrees and mitigate them from the representations. However, there exist two deficiencies: (1) when measuring the shortcut degrees, most strategies only use statistical metrics on a single aspect (i.e., item frequency on item and user frequency on user aspect), failing to accommodate the compositional degree of a user-item pair; (2) when mitigating shortcuts, many strategies assume that the test distribution is known in advance. This results in low-quality debiased representations. Worse still, these strategies achieve OOD generalizability with a sacrifice on ID performance. In this work, we present a simple yet effective debiasing strategy, PopGo, which quantifies and reduces the interaction-wise popularity shortcut without any assumptions on the test data. It first learns a shortcut model, which yields a shortcut degree of a user-item pair based on their popularity representations. Then, it trains the CF model by adjusting the predictions with the interaction-wise shortcut degrees. By taking both causal- and information-theoretical looks at PopGo, we can justify why it encourages the CF model to capture the critical popularity-agnostic features while leaving the spurious popularity-relevant patterns out. We use PopGo to debias two high-performing CF models (MF, LightGCN) on four benchmark datasets. On both ID and OOD test sets, PopGo achieves significant gains over the state-of-the-art debiasing strategies (e.g., DICE, MACR).

References in corpus (11)

• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Session-based Social Recommendation via Dynamic Graph Attention Networks
• Connecting User and Item Perspectives in Popularity Debiasing for Collaborative Recommendation
• ESAM: Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance
• On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law
• Invariant Collaborative Filtering to Popularity Distribution Shift
• CausPref: Causal Preference Learning for Out-of-Distribution Recommendation
• Introspective Distillation for Robust Question Answering
• Debiasing Neighbor Aggregation for Graph Neural Network in Recommender Systems
• Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss
• Item Recommendation from Implicit Feedback