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• W4361804600 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." @default.
• W4361804600 created "2023-04-05" @default.
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• W4361804600 date "2023-04-30" @default.
• W4361804600 modified "2023-10-18" @default.
• W4361804600 title "Cooperative Retriever and Ranker in Deep Recommenders" @default.
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• W4361804600 doi "https://doi.org/10.1145/3543507.3583422" @default.
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