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.
@article{arxiv.2206.14649,
title = {Cooperative Retriever and Ranker in Deep Recommenders},
author = {Xu Huang and Defu Lian and Jin Chen and Zheng Liu and Xing Xie and Enhong Chen},
journal= {arXiv preprint arXiv:2206.14649},
year = {2023}
}