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- W4384648240 abstract "Recommender system (RS) is widely applied in a multitude of scenarios to aid individuals obtaining the information they require efficiently. At the same time, the prevalence of popularity bias in such systems has become a widely acknowledged issue. To address this challenge, we propose a novel method named Model-Agnostic Popularity Debias Training Framework (MDTF). It consists of two basic modules including 1) General Ranking Model (GRM), which is model-agnostic and can be implemented as any ranking models; and 2) Popularity Debias Module (PDM), which estimates the impact of the competitiveness and popularity of candidate items on the CTR, by utilizing the feedback of cold-start users to re-weigh the loss in GRM. MDTF seamlessly integrates these two modules in an end-to-end multi-task learning framework. Extensive experiments on both real-world offline dataset and online A/B test demonstrate its superiority over state-of-the-art methods." @default.
- W4384648240 created "2023-07-19" @default.
- W4384648240 creator A5002273131 @default.
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- W4384648240 date "2023-07-18" @default.
- W4384648240 modified "2023-09-26" @default.
- W4384648240 title "A Model-Agnostic Popularity Debias Training Framework for Click-Through Rate Prediction in Recommender System" @default.
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- W4384648240 doi "https://doi.org/10.1145/3539618.3591939" @default.
- W4384648240 hasPublicationYear "2023" @default.
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