Both Supply and Precision: Sample Debias and Ranking Consistency Joint Learning for Large Scale Pre-Ranking System

Abstract

Cascade ranking architecture, composed of matching, pre-ranking, ranking and re-ranking stages, is usually adopted to balance the efficiency and effectiveness in real-world recommendation system (RS). As the middle stage of RS, pre-ranking aims to quickly filter out the low-quality items selected at the matching stage and then forwarding high-quality items to the ranking stage. Existing pre-ranking approaches mainly endure two problems 1) Sample Selection Bias (SSB) problem, which heavily limits the performance improvement of filtering out low-quality items owing to ignoring the data flow between stages; and 2) Ranking Consistency (RC) problem, which may cause the ranked lists of the ranking stage and previous pre-ranking stage to be inconsistent. As a result, the competitive items with high scores at the ranking stage may not be selected because of low scores at the pre-ranking stage. These both two problems may cause sub-optimal performances, but previous works usually only focus on the one of them. In this paper, we propose a novel Sample Debias and Ranking Consistency Joint Learning Framework (SDCL) to jointly alleviate SSB and RC problems. SDCL consists of two main modules including 1) Multi-Task Distillation Module (MTD), which enhances the ability of identifying high-quality items by distilling knowledge across all tasks simultaneously from the more complex ranking model which jointly trained with the pre-ranking model; and 2) Adaptive Negative Sample Learning Module (ANSL), which improves the performance of filtering out low-quality items by adaptively adjusting negative samples learning weights based on the current performance of model. SDCL seamlessly integrates two modules in an end-to-end multi-task learning framework. Evaluations on both real-world large-scale traffic logs and online A/B test demonstrate the efficacy and superiority of SDCL.