  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).
