SelfCF: A Simple Framework for Self-supervised Collaborative Filtering
- URL: http://arxiv.org/abs/2107.03019v3
- Date: Sun, 30 Apr 2023 09:47:26 GMT
- Title: SelfCF: A Simple Framework for Self-supervised Collaborative Filtering
- Authors: Xin Zhou, Aixin Sun, Yong Liu, Jie Zhang, Chunyan Miao
- Abstract summary: Collaborative filtering (CF) is widely used to learn informative latent representations of users and items from observed interactions.
We propose a self-supervised collaborative filtering framework (SelfCF) that is specially designed for recommender scenario with implicit feedback.
We show that SelfCF can boost up the accuracy by up to 17.79% on average, compared with a self-supervised framework BUIR.
- Score: 72.68215241599509
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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