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Learning to rank for freshness and relevance

Authors:

Milad Shokouhi,

Brian D. DavisonAuthors Info & Claims

SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

Pages 95 - 104

https://doi.org/10.1145/2009916.2009933

Published: 24 July 2011 Publication History

Abstract

Freshness of results is important in modern web search. Failing to recognize the temporal aspect of a query can negatively affect the user experience, and make the search engine appear stale. While freshness and relevance can be closely related for some topics (e.g., news queries), they are more independent in others (e.g., time insensitive queries). Therefore, optimizing one criterion does not necessarily improve the other, and can even do harm in some cases.

We propose a machine-learning framework for simultaneously optimizing freshness and relevance, in which the trade-off is automatically adaptive to query temporal characteristics. We start by illustrating different temporal characteristics of queries, and the features that can be used for capturing these properties. We then introduce our supervised framework that leverages the temporal profile of queries (inferred from pseudo-feedback documents) along with the other ranking features to improve both freshness and relevance of search results. Our experiments on a large archival web corpus demonstrate the efficacy of our techniques.

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Cited By

Peikos GPasi G(2024)A systematic review of multidimensional relevance estimation in information retrievalWIREs Data Mining and Knowledge Discovery10.1002/widm.154114:5Online publication date: 7-May-2024
https://doi.org/10.1002/widm.1541
Paparella VAnelli VNardini FPerego RDi Noia TFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Post-hoc Selection of Pareto-Optimal Solutions in Search and RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615010(2013-2023)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615010
Yang HGonçalves T(2023)Field features: The impact in learning to rank approachesApplied Soft Computing10.1016/j.asoc.2023.110183138(110183)Online publication date: May-2023
https://doi.org/10.1016/j.asoc.2023.110183
Show More Cited By

Index Terms

Learning to rank for freshness and relevance
1. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

July 2011

1374 pages

ISBN:9781450307574

DOI:10.1145/2009916

General Chairs:
Wei-Ying Ma
Microsoft Research Asia, China
,
Jian-Yun Nie
University of Montreal, Canada
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Research, Spain
,
Tat-Seng Chua
National University of Singapore
,
W. Bruce Croft
University of Massachusetts, Amherst, USA

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 July 2011

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SIGIR '11

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SIGIR

SIGIR '11: The 34th International ACM SIGIR conference on research and development in Information Retrieval

July 24 - 28, 2011

Beijing, China

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Peikos GPasi G(2024)A systematic review of multidimensional relevance estimation in information retrievalWIREs Data Mining and Knowledge Discovery10.1002/widm.154114:5Online publication date: 7-May-2024
https://doi.org/10.1002/widm.1541
Paparella VAnelli VNardini FPerego RDi Noia TFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Post-hoc Selection of Pareto-Optimal Solutions in Search and RecommendationProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615010(2013-2023)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615010
Yang HGonçalves T(2023)Field features: The impact in learning to rank approachesApplied Soft Computing10.1016/j.asoc.2023.110183138(110183)Online publication date: May-2023
https://doi.org/10.1016/j.asoc.2023.110183
Jatowt ASato MDraxl SDuan YCampos RYoshikawa M(2023)Is this news article still relevant? Ranking by contemporary relevance in archival searchInternational Journal on Digital Libraries10.1007/s00799-023-00377-y25:2(197-216)Online publication date: 28-Jul-2023
https://doi.org/10.1007/s00799-023-00377-y
Zhang LJoho HYu H(2022)Semantic Modelling of Document Focus-Time for Temporal Information RetrievalCompanion Proceedings of the Web Conference 202210.1145/3487553.3524668(896-902)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3487553.3524668
Zhang PGao HHu ZYang MSong DWang JHou YHu B(2022)A bias–variance evaluation framework for information retrieval systemsInformation Processing and Management: an International Journal10.1016/j.ipm.2021.10274759:1Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1016/j.ipm.2021.102747
Yang HGupta PFernández Galán RBu DJia DDemartini GZuccon GCulpepper JHuang ZTong H(2021)Seasonal Relevance in E-Commerce SearchProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3481951(4293-4301)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3481951
Tsagkias MKing TKallumadi SMurdock Vde Rijke M(2021)Challenges and research opportunities in eCommerce search and recommendationsACM SIGIR Forum10.1145/3451964.345196654:1(1-23)Online publication date: 19-Feb-2021
https://dl.acm.org/doi/10.1145/3451964.3451966
Zhuang SZuccon GDiaz FShah CSuel TCastells PJones RSakai T(2021)How do Online Learning to Rank Methods Adapt to Changes of Intent?Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462937(911-920)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3462937
Sato MJatowt ADuan YCampos RYoshikawa M(2021)Estimating Contemporary Relevance of Past News2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL)10.1109/JCDL52503.2021.00019(70-79)Online publication date: Sep-2021
https://doi.org/10.1109/JCDL52503.2021.00019
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