  Deep candidate generation (DCG) that narrows down the collection of relevant
items from billions to hundreds via representation learning has become
prevalent in industrial 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. However, live
recommender systems face severe exposure bias and have a vocabulary several
orders of magnitude larger than that of natural language, implying that MLE
will preserve and even exacerbate the exposure bias in the long run in order to
faithfully fit the observed samples. In this paper, we theoretically prove that
a popular choice of contrastive loss is equivalent to reducing the exposure
bias via inverse propensity weighting, which provides a new perspective for
understanding the effectiveness of contrastive learning. Based on the
theoretical discovery, we design CLRec, a contrastive learning method to
improve DCG in terms of fairness, effectiveness and efficiency in recommender
systems with extremely large candidate size. We further improve upon CLRec and
propose Multi-CLRec, for accurate multi-intention aware bias reduction. Our
methods have been successfully deployed in Taobao, where at least four-month
online A/B tests and offline analyses demonstrate its substantial improvements,
including a dramatic reduction in the Matthew effect.
