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Learning contrastive representations from pairwise comparisons has achievedremarkable success in various fields, such as natural language processing,computer vision, and information retrieval. Collaborative filtering algorithmsbased on pairwise learning also rooted in this paradigm. A significant concernis the absence of labels for negative instances in implicit feedback data,which often results in the random selected negative instances contains falsenegatives and inevitably, biased embeddings. To address this issue, weintroduce a novel correction method for sampling bias that yields a modifiedloss for pairwise learning called debiased pairwise loss (DPL). The key ideaunderlying DPL is to correct the biased probability estimates that result fromfalse negatives, thereby correcting the gradients to approximate those of fullysupervised data. The implementation of DPL only requires a small modificationof the codes. Experimental studies on five public datasets validate theeffectiveness of proposed learning method.

Debiased Pairwise Learning from Positive-Unlabeled Implicit Feedback