  Contrastive Learning (CL) performances as a rising approach to address the
challenge of sparse and noisy recommendation data. Although having achieved
promising results, most existing CL methods only perform either hand-crafted
data or model augmentation for generating contrastive pairs to find a proper
augmentation operation for different datasets, which makes the model hard to
generalize. Additionally, since insufficient input data may lead the encoder to
learn collapsed embeddings, these CL methods expect a relatively large number
of training data (e.g., large batch size or memory bank) to contrast. However,
not all contrastive pairs are always informative and discriminative enough for
the training processing. Therefore, a more general CL-based recommendation
model called Meta-optimized Contrastive Learning for sequential Recommendation
(MCLRec) is proposed in this work. By applying both data augmentation and
learnable model augmentation operations, this work innovates the standard CL
framework by contrasting data and model augmented views for adaptively
capturing the informative features hidden in stochastic data augmentation.
Moreover, MCLRec utilizes a meta-learning manner to guide the updating of the
model augmenters, which helps to improve the quality of contrastive pairs
without enlarging the amount of input data. Finally, a contrastive
regularization term is considered to encourage the augmentation model to
generate more informative augmented views and avoid too similar contrastive
pairs within the meta updating. The experimental results on commonly used
datasets validate the effectiveness of MCLRec.
