Taming the One-Epoch Phenomenon in Online Recommendation System by Two-stage Contrastive ID Pre-training

• URL: http://arxiv.org/abs/2508.18700v1
• Date: Tue, 26 Aug 2025 06:06:21 GMT
• Title: Taming the One-Epoch Phenomenon in Online Recommendation System by Two-stage Contrastive ID Pre-training
• Authors: Yi-Ping Hsu, Po-Wei Wang, Chantat Eksombatchai, Jiajing Xu,
• Abstract summary: We introduce a novel two-stage training strategy that incorporates a pre-training phase using a minimal model with contrastive loss.<n>Our offline experiments demonstrate that multi-epoch training during the pre-training phase does not lead to overfitting.
• Score: 1.1549572298362782
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: ID-based embeddings are widely used in web-scale online recommendation systems. However, their susceptibility to overfitting, particularly due to the long-tail nature of data distributions, often limits training to a single epoch, a phenomenon known as the "one-epoch problem." This challenge has driven research efforts to optimize performance within the first epoch by enhancing convergence speed or feature sparsity. In this study, we introduce a novel two-stage training strategy that incorporates a pre-training phase using a minimal model with contrastive loss, enabling broader data coverage for the embedding system. Our offline experiments demonstrate that multi-epoch training during the pre-training phase does not lead to overfitting, and the resulting embeddings improve online generalization when fine-tuned for more complex downstream recommendation tasks. We deployed the proposed system in live traffic at Pinterest, achieving significant site-wide engagement gains.

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