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Contrastive self-supervised learning provides a dominant and effective approach for mitigating data sparsity and cold-start issues in recommender systems, and its various methods can be unified under a taxonomy of view generation strategies, contrastive tasks, and contrastive objectives. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

Contrastive Self-supervised Learning in Recommender Systems: A Survey

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