  Recent years have witnessed the great success of self-supervised learning
(SSL) in recommendation systems. However, SSL recommender models are likely to
suffer from spurious correlations, leading to poor generalization. To mitigate
spurious correlations, existing work usually pursues ID-based SSL
recommendation or utilizes feature engineering to identify spurious features.
Nevertheless, ID-based SSL approaches sacrifice the positive impact of
invariant features, while feature engineering methods require high-cost human
labeling. To address the problems, we aim to automatically mitigate the effect
of spurious correlations. This objective requires to 1) automatically mask
spurious features without supervision, and 2) block the negative effect
transmission from spurious features to other features during SSL. To handle the
two challenges, we propose an invariant feature learning framework, which first
divides user-item interactions into multiple environments with distribution
shifts and then learns a feature mask mechanism to capture invariant features
across environments. Based on the mask mechanism, we can remove the spurious
features for robust predictions and block the negative effect transmission via
mask-guided feature augmentation. Extensive experiments on two datasets
demonstrate the effectiveness of the proposed framework in mitigating spurious
correlations and improving the generalization abilities of SSL models. The code
is available at https://github.com/Linxyhaha/IFL.
