mlsanityData to 14 Oct 2026

Cooperative Retriever and Ranker in Deep Recommenders

arXiv 2206.14649 ↗First posted 28 Jun 2022cs.IR cs.LGcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 37 of its 71 references that are in the map. First posted on arXiv 28 Jun 2022.

Builds on

37 of 71 references.

References · 37

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25 more
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Cited by year

Citing papers per year: 2023: 3; 2024: 0; 2025: 3.

6 papers.

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  2. 2023CONVERT:Contrastive Graph Clustering with Reliable Augmentationcited by 23
  3. 2023Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systemscited by 6
  4. 2025GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Modelscited by 3
  5. 2025NDCG-Consistent Softmax Approximation with Accelerated Convergencecited by 0
  6. 2025Bidding-Aware Retrieval for Multi-Stage Consistency in Online Advertisingcited by 0