Before revision:  In this paper, we introduce CLRec, a Contrastive Learning paradigm that has been successfully deployed in a real-world massive recommender system, to alleviate exposure bias in DCG. We theoretically prove that a popular choice of contrastive loss is equivalently reducing the exposure bias via inverse propensity scoring , which provides a new perspective on the effectiveness of contrastive learning.
After revision:  In this paper, we theoretically prove that a popular choice of contrastive loss is equivalently reducing the exposure bias via inverse propensity scoring , which provides a new perspective on the effectiveness of contrastive learning.