Adaptive Re-Ranking as an Information-Seeking Agent

MacAvaney, S. , Tonellotto, N. and Macdonald, C. (2022) Adaptive Re-Ranking as an Information-Seeking Agent. In: First Workshop on Proactive and Agent-Supported Information Retrieval (PASIR 2022), 17-21 October 2022, Atlanta, GA, USA,

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Re-ranking systems are typically limited by the recall of the initial retrieval function. A recent work proposed adaptive re-ranking, which modifies the re-ranking loop to progressively prioritise documents likely to receive high scores based on the highest scoring ones thus far. The original work framed this process as an incarnation of the well-established clustering hypothesis. In this work, we argue that the approach can also be framed as an information-seeking agent. From this perspective, we explore several variations of the graph-based adaptive re-ranking algorithm and find that there is substantial room for improvement by modifying the agent. However, the agents that we explore are more sensitive to the new parameters they introduce than the simple-yet-effective approach proposed in the original adaptive re-ranking work.

Item Type:Conference Proceedings
Additional Information:Organized as a part of CIKM 2022.
Glasgow Author(s) Enlighten ID:MacAvaney, Dr Sean and Macdonald, Professor Craig
Authors: MacAvaney, S., Tonellotto, N., and Macdonald, C.
College/School:College of Science and Engineering > School of Computing Science
Research Centre:College of Science and Engineering > School of Computing Science > IDA Section > GPU Cluster
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in Proceedings of the CIKM 2022 Workshops
Publisher Policy:Reproduced under a Creative Commons License
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
300982Exploiting Closed-Loop Aspects in Computationally and Data Intensive AnalyticsRoderick Murray-SmithEngineering and Physical Sciences Research Council (EPSRC)EP/R018634/1Computing Science