Cooperative Retriever and Ranker in Deep Recommenders

Abstract

Deep recommender systems (DRS) are intensively applied in modern web services. To deal with the massive web contents, DRS employs a two-stage workflow: retrieval and ranking, to generate its recommendation results. The retriever aims to select a small set of relevant candidates from the entire items with high efficiency; while the ranker, usually more precise but time-consuming, is supposed to further refine the best items from the retrieved candidates. Traditionally, the two components are trained either independently or within a simple cascading pipeline, which is prone to poor collaboration effect. Though some latest works suggested to train retriever and ranker jointly, there still exist many severe limitations: item distribution shift between training and inference, false negative, and misalignment of ranking order. As such, it remains to explore effective collaborations between retriever and ranker.

arXiv comment12pages, 4 figures, WWW'23

References in corpus (6)

• Graph Convolutional Neural Networks for Web-Scale Recommender Systems
• Learning Tree-based Deep Model for Recommender Systems
• DE-RRD: A Knowledge Distillation Framework for Recommender System
• Bidirectional Distillation for Top-K Recommender System
• Linear-Time Self Attention with Codeword Histogram for Efficient Recommendation
• On component interactions in two-stage recommender systems