Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems

20 May 2020 Chang Zhou Jianxin Ma Jianwei Zhang Jingren Zhou Hongxia Yang

Deep candidate generation (DCG) that narrows down the collection of relevant items from billions to hundreds via representation learning is essential to large-scale recommender systems. Standard approaches approximate maximum likelihood estimation (MLE) through sampling for better scalability and address the problem of DCG in a way similar to language modeling... (read more)

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