RLIRank: Learning to Rank with Reinforcement Learning for Dynamic Search-Reference-Cited by-同舟云学术

RLIRank: Learning to Rank with Reinforcement Learning for Dynamic Search

Published:2020-04-19 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of The Web Conference 2020
language:
Short-container-title:

Author:

Zhou Jianghong¹,Agichtein Eugene¹

Affiliation:

1. Emory University

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3366423.3380047

Reference32 articles.

1. Ameer Albahem Damiano Spina Lawrence Cavedon and Falk Scholer. 2016. RMIT@ TREC 2016 Dynamic Domain Track: Exploiting Passage Representation for Retrieval and Relevance Feedback.. In TREC. Ameer Albahem Damiano Spina Lawrence Cavedon and Falk Scholer. 2016. RMIT@ TREC 2016 Dynamic Domain Track: Exploiting Passage Representation for Retrieval and Relevance Feedback.. In TREC.

2. Learning to rank using gradient descent

3. Learning to rank

4. Novelty and diversity in information retrieval evaluation

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