Contrastive Information Transfer for Pre-Ranking Systems

TL;DR

This paper introduces a Contrastive Information Transfer framework that enhances pre-ranking models in search and recommender systems by leveraging rich information from ranking models, leading to improved performance and business metrics.

Contribution

The paper proposes a novel contrastive learning approach for transferring information from ranking to pre-ranking models, addressing selection bias and boosting recall metrics.

Findings

CIT outperforms competitive models in offline experiments.

Online A/B testing shows 0.63% CTR and 1.64% VBR improvements.

Model deployment contributed to significant business growth.

Abstract

Real-word search and recommender systems usually adopt a multi-stage ranking architecture, including matching, pre-ranking, ranking, and re-ranking. Previous works mainly focus on the ranking stage while very few focus on the pre-ranking stage. In this paper, we focus on the information transfer from ranking to pre-ranking stage. We propose a new Contrastive Information Transfer (CIT) framework to transfer useful information from ranking model to pre-ranking model. We train the pre-ranking model to distinguish the positive pair of representation from a set of positive and negative pairs with a contrastive objective. As a consequence, the pre-ranking model can make full use of rich information in ranking model's representations. The CIT framework also has the advantage of alleviating selection bias and improving the performance of recall metrics, which is crucial for pre-ranking models. We conduct extensive experiments including offline datasets and online A/B testing. Experimental results show that CIT achieves superior results than competitive models. In addition, a strict online A/B testing at one of the world's largest E-commercial platforms shows that the proposed model achieves 0.63\% improvements on CTR and 1.64\% improvements on VBR. The proposed model now has been deployed online and serves the main traffic of this system, contributing a remarkable business growth.