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SelfCF: A Simple Framework for Self-supervised Collaborative Filtering
[article]
2022
arXiv
pre-print
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
arXiv:2107.03019v2
fatcat:gwuduuttdrgdhhwd4atbtrbmvi