How do Online Learning to Rank Methods Adapt to Changes of Intent?

Author:

Zhuang Shengyao1,Zuccon Guido1

Affiliation:

1. The University of Queensland, Brisbane, QLD, Australia

Funder

Australian Research Council

Publisher

ACM

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Learning to Rank for Non Independent and Identically Distributed Datasets;Proceedings of the 2024 ACM SIGIR International Conference on Theory of Information Retrieval;2024-08-02

2. Adversarial Density Ratio Estimation for Change Point Detection;Proceedings of the 32nd ACM International Conference on Information and Knowledge Management;2023-10-21

3. Reinforcement online learning to rank with unbiased reward shaping;Information Retrieval Journal;2022-08-04

4. Is Non-IID Data a Threat in Federated Online Learning to Rank?;Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval;2022-07-06

5. Continually Adaptive Neural Retrieval Across the Legal, Patent and Health Domain;Lecture Notes in Computer Science;2022

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