  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.
