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Socially-Aware Self-Supervised Tri-Training for Recommendation
2021
Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
Self-supervised learning (SSL), which can automatically generate ground-truth samples from raw data, holds vast potential to improve recommender systems. Most existing SSL-based methods perturb the raw data graph with uniform node/edge dropout to generate new data views and then conduct the self-discrimination based contrastive learning over different views to learn generalizable representations. Under this scheme, only a bijective mapping is built between nodes in two different views, which
doi:10.1145/3447548.3467340
fatcat:bria3dcw4jf5pb6ktqp5kmqeay