  Collaborative Filtering (CF) models, despite their great success, suffer from
severe performance drops due to popularity distribution shifts, where these
changes are ubiquitous and inevitable in real-world scenarios. Unfortunately,
most leading popularity debiasing strategies, rather than tackling the
vulnerability of CF models to varying popularity distributions, require prior
knowledge of the test distribution to identify the degree of bias and further
learn the popularity-entangled representations to mitigate the bias.
Consequently, these models result in significant performance benefits in the
target test set, while dramatically deviating the recommendation from users'
true interests without knowing the popularity distribution in advance. In this
work, we propose a novel learning framework, Invariant Collaborative Filtering
(InvCF), to discover disentangled representations that faithfully reveal the
latent preference and popularity semantics without making any assumption about
the popularity distribution. At its core is the distillation of unbiased
preference representations (i.e., user preference on item property), which are
invariant to the change of popularity semantics, while filtering out the
popularity feature that is unstable or outdated. Extensive experiments on five
benchmark datasets and four evaluation settings (i.e., synthetic long-tail,
unbiased, temporal split, and out-of-distribution evaluations) demonstrate that
InvCF outperforms the state-of-the-art baselines in terms of popularity
generalization ability on real recommendations. Visualization studies shed
light on the advantages of InvCF for disentangled representation learning. Our
codes are available at https://github.com/anzhang314/InvCF.
