research-article

Probabilistic models of ranking novel documents for faceted topic retrieval

Authors:

Ben Carterette,

Praveen ChandarAuthors Info & Claims

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

Pages 1287 - 1296

https://doi.org/10.1145/1645953.1646116

Published: 02 November 2009 Publication History

Abstract

Traditional models of information retrieval assume documents are independently relevant. But when the goal is retrieving diverse or novel information about a topic, retrieval models need to capture dependencies between documents. Such tasks require alternative evaluation and optimization methods that operate on different types of relevance judgments. We define faceted topic retrieval as a particular novelty-driven task with the goal of finding a set of documents that cover the different facets of an information need. A faceted topic retrieval system must be able to cover as many facets as possible with the smallest number of documents. We introduce two novel models for faceted topic retrieval, one based on pruning a set of retrieved documents and one based on retrieving sets of documents through direct optimization of evaluation measures. We compare the performance of our models to MMR and the probabilistic model due to Zhai et al. on a set of 60 topics annotated with facets, showing that our models are competitive.

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Cited By

Zhang RAi QWu YMa YLiu Y(2023)Result Diversification for Legal case RetrievalProceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3624918.3625319(158-168)Online publication date: 26-Nov-2023
https://dl.acm.org/doi/10.1145/3624918.3625319
Yu PRahimi RHuang ZAllan JFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Search Result Diversification Using Query Aspects as BottlenecksProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615050(3040-3051)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615050
Reuben MElyashar APuzis R(2022)Iterative query selection for opaque search engines with pseudo relevance feedbackExpert Systems with Applications10.1016/j.eswa.2022.117027201(117027)Online publication date: Sep-2022
https://doi.org/10.1016/j.eswa.2022.117027
Show More Cited By

Index Terms

Probabilistic models of ranking novel documents for faceted topic retrieval
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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cover image ACM Conferences

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

November 2009

2162 pages

ISBN:9781605585123

DOI:10.1145/1645953

General Chairs:
David Cheung
University of Hong Kong, Hong Kong
,
Il-Yeol Song
Drexel University, USA
,
Program Chairs:
Wesley Chu
UCLA, USA
,
Xiaohua Hu
Drexel University, USA
,
Jimmy Lin
University of Maryland, USA

Copyright © 2009 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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Published: 02 November 2009

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November 2 - 6, 2009

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Cited By

Zhang RAi QWu YMa YLiu Y(2023)Result Diversification for Legal case RetrievalProceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3624918.3625319(158-168)Online publication date: 26-Nov-2023
https://dl.acm.org/doi/10.1145/3624918.3625319
Yu PRahimi RHuang ZAllan JFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Search Result Diversification Using Query Aspects as BottlenecksProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615050(3040-3051)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615050
Reuben MElyashar APuzis R(2022)Iterative query selection for opaque search engines with pseudo relevance feedbackExpert Systems with Applications10.1016/j.eswa.2022.117027201(117027)Online publication date: Sep-2022
https://doi.org/10.1016/j.eswa.2022.117027
Hashemi HZamani HCroft WDemartini GZuccon GCulpepper JHuang ZTong H(2021)Learning Multiple Intent Representations for Search QueriesProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482445(669-679)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482445
Dumitrescu ASantini S(2021)Full coverage of a reader's interests in context‐based information filteringJournal of the Association for Information Science and Technology10.1002/asi.2447072:8(1011-1027)Online publication date: 5-Jul-2021
https://dl.acm.org/doi/10.1002/asi.24470
Sarwar SAddanki RMontazeralghaem APal SAllan JBalog KSetty VLioma CLiu YZhang MBerberich K(2020)Search Result Diversification with Guarantee of Topic ProportionalityProceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval10.1145/3409256.3409839(53-60)Online publication date: 14-Sep-2020
https://dl.acm.org/doi/10.1145/3409256.3409839
Meymandpour RDavis J(2019)Measuring the diversity of recommendations: a preference-aware approach for evaluating and adjusting diversityKnowledge and Information Systems10.1007/s10115-019-01371-0Online publication date: 19-Jun-2019
https://doi.org/10.1007/s10115-019-01371-0
Malik SKim SKoh EAlameda-Pineda XRedi MSebe NChang SLuo J(2018)Perceptual Similarity Ranking of Temporal Heatmaps Using Convolutional Neural NetworksProceedings of the 2018 Workshop on Understanding Subjective Attributes of Data, with the Focus on Evoked Emotions10.1145/3267799.3267803(25-31)Online publication date: 15-Oct-2018
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Vikraman LCroft WO'Connor BSong DLiu TSun LBruza PMelucci MSebastiani FYang G(2018)Exploring Diversification In Non-factoid Question AnsweringProceedings of the 2018 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3234944.3234973(223-226)Online publication date: 10-Sep-2018
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Wu YLiu YChen FZhang MMa SSong DLiu TSun LBruza PMelucci MSebastiani FYang G(2018)Beyond Greedy SearchProceedings of the 2018 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3234944.3234967(99-106)Online publication date: 10-Sep-2018
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