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.

翻译：对比学习（CL）作为应对稀疏与噪声推荐数据挑战的新兴方法，已展现出显著潜力。尽管现有CL方法取得了可喜成果，但其主要依赖人工设计的数据增强或模型增强来生成对比对，导致针对不同数据集难以找到合适的增强操作，模型泛化能力受限。此外，由于输入数据不足可能导致编码器学习到坍缩的嵌入表示，这些CL方法需要大量训练数据（如大批次大小或记忆库）进行对比。然而，并非所有对比对在训练过程中都具有充分的信息性与判别性。为此，本文提出一种更具通用性的基于对比学习的推荐模型——面向序列推荐的元优化对比学习（MCLRec）。通过同时应用数据增强与可学习模型增强操作，本工作创新性地将标准CL框架扩展为数据增强视图与模型增强视图的对比学习，从而自适应捕获随机数据增强中隐藏的信息特征。同时，MCLRec采用元学习方式指导模型增强器的更新，在不增加输入数据量的前提下提升对比对质量。最后，引入对比正则化项，激励增强模型生成更具信息性的增强视图，并避免元更新过程中产生过于相似的对比对。在常用数据集上的实验结果验证了MCLRec的有效性。