Cooperative Retriever and Ranker in Deep Recommenders

• URL: http://arxiv.org/abs/2206.14649v2
• Date: Wed, 29 Mar 2023 10:01:09 GMT
• Title: Cooperative Retriever and Ranker in Deep Recommenders
• Authors: Xu Huang, Defu Lian, Jin Chen, Zheng Liu, Xing Xie, Enhong Chen
• Abstract summary: Deep recommender systems (DRS) are intensively applied in modern web services. DRS employs a two-stage workflow: retrieval and ranking, to generate its recommendation results. It remains to explore effective collaborations between retriever and ranker.
• Score: 75.35463122701135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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