(Debiased) Contrastive Learning Loss for Recommendation (Technical Report)
This work addresses bias in recommendation systems, which is an incremental improvement for enhancing model fairness and performance.
The paper tackles the problem of bias in recommendation system losses by introducing debiased versions of InfoNCE and mutual information neural estimator (MINE), and theoretically certifying that popular linear models like iALS and EASE are inherently debiased, with empirical results showing these debiased losses outperform existing biased ones.
In this paper, we perform a systemic examination of the recommendation losses, including listwise (softmax), pairwise(BPR), and pointwise (mean-squared error, MSE, and Cosine Contrastive Loss, CCL) losses through the lens of contrastive learning. We introduce and study both debiased InfoNCE and mutual information neural estimator (MINE), for the first time, under the recommendation setting. We also relate and differentiate these two losses with the BPR loss through the lower bound analysis. Furthermore, we present the debiased pointwise loss (for both MSE and CCL) and theoretically certify both iALS and EASE, two of the most popular linear models, are inherently debiased. The empirical experimental results demonstrate the effectiveness of the debiased losses and newly introduced mutual-information losses outperform the existing (biased) ones.