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C2AL: Cohort-Contrastive Auxiliary Learning for Large-scale Recommendation Systems
Mertcan Cokbas, Ziteng Liu, Zeyi Tao, Chengkai Zhang, Elder Veliz, Qin Huang, Ellie Wen, Huayu Li, Qiang Jin, Murat Duman, Benjamin Au, Guy Lebanon, Sagar Chordia
https://arxiv.org/abs/2510.02215

C2AL: Cohort-Contrastive Auxiliary Learning for Large-scale Recommendation Systems
Training large-scale recommendation models under a single global objective implicitly assumes homogeneity across user populations. However, real-world data are composites of heterogeneous cohorts with distinct conditional distributions. As models increase in scale and complexity and as more data is used for training, they become dominated by central distribution patterns, neglecting head and tail regions. This imbalance limits the model's learning ability and can result in inactive attention we…