  Many Click-Through Rate (CTR) prediction works focused on designing advanced
architectures to model complex feature interactions but neglected the
importance of feature representation learning, e.g., adopting a plain embedding
layer for each feature, which results in sub-optimal feature representations
and thus inferior CTR prediction performance. For instance, low frequency
features, which account for the majority of features in many CTR tasks, are
less considered in standard supervised learning settings, leading to
sub-optimal feature representations. In this paper, we introduce
self-supervised learning to produce high-quality feature representations
directly and propose a model-agnostic Contrastive Learning for CTR (CL4CTR)
framework consisting of three self-supervised learning signals to regularize
the feature representation learning: contrastive loss, feature alignment, and
field uniformity. The contrastive module first constructs positive feature
pairs by data augmentation and then minimizes the distance between the
representations of each positive feature pair by the contrastive loss. The
feature alignment constraint forces the representations of features from the
same field to be close, and the field uniformity constraint forces the
representations of features from different fields to be distant. Extensive
experiments verify that CL4CTR achieves the best performance on four datasets
and has excellent effectiveness and compatibility with various representative
baselines.
