SelfCF: A Simple Framework for Self-supervised Collaborative Filtering

Understand

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.