Collaborative filtering (CF) is widely used to learn informative latent representations of users and items from observed interactions. Existing CF-based methods commonly adopt negative sampling to discriminate different items. Training with negative sampling on large datasets is computationally expensive. Further, negative items should be carefully sampled under the defined distribution, in order to avoid selecting an observed positive item in the training dataset. Unavoidably, some negative items sampled from the training dataset could be positive in the test set. In this paper, we propose a self-supervised collaborative filtering framework (SelfCF), that is specially designed for recommender scenario with implicit feedback. The proposed SelfCF framework simplifies the Siamese networks and can be easily applied to existing deep-learning based CF models, which we refer to as backbone networks. The main idea of SelfCF is to augment the output embeddings generated by backbone networks, because it is infeasible to augment raw input of user/item ids. We propose and study three output perturbation techniques that can be applied to different types of backbone networks including both traditional CF models and graph-based models. The framework enables learning informative representations of users and items without negative samples, and is agnostic to the encapsulated backbones. We conduct comprehensive experiments on four datasets to show that our framework may achieve even better recommendation accuracy than the encapsulated supervised counterpart with a 2$\times$--4$\times$ faster training speed. We also show that SelfCF can boost up the accuracy by up to 17.79% on average, compared with a self-supervised framework BUIR.

翻译：协同过滤（CF）广泛用于从观测到的交互中学习用户和物品的潜在表示。现有基于CF的方法通常采用负采样来区分不同物品，但在大规模数据集上使用负采样进行训练的代价高昂。此外，为避免从训练数据集中采样到已观测的正样本，需按特定分布谨慎选择负样本。然而，从训练集中采样的某些负样本在测试集中仍可能实际为正样本。本文提出一种专门针对隐式反馈推荐场景的自监督协同过滤框架（SelfCF）。该框架简化了孪生网络结构，可便捷地应用于现有基于深度学习的CF模型（我们称之为骨干网络）。SelfCF的核心思想是对骨干网络生成的输出嵌入进行增强，因为直接增强用户/物品的原始ID输入并不可行。我们提出并研究了三种输出扰动技术，可适用于包含传统CF模型和图模型在内的多种骨干网络类型。该框架无需负样本即可学习用户和物品的信息表示，且与所封装的骨干网络无关。我们在四个数据集上的综合实验表明，该框架能在训练速度提升2-4倍的同时，达到甚至超过带监督的对应模型的推荐精度。此外，与自监督框架BUIR相比，SelfCF平均可提升最高达17.79%的准确率。