review-article

Personalization in text information retrieval: : A survey

Authors:

Nicholas J. BelkinAuthors Info & Claims

Journal of the Association for Information Science and Technology, Volume 71, Issue 3

Pages 349 - 369

https://doi.org/10.1002/asi.24234

Published: 28 January 2020 Publication History

Abstract

Personalization of information retrieval (PIR) is aimed at tailoring a search toward individual users and user groups by taking account of additional information about users besides their queries. In the past two decades or so, PIR has received extensive attention in both academia and industry. This article surveys the literature of personalization in text retrieval, following a framework for aspects or factors that can be used for personalization. The framework consists of additional information about users that can be explicitly obtained by asking users for their preferences, or implicitly inferred from users' search behaviors. Users' characteristics and contextual factors such as tasks, time, location, etc., can be helpful for personalization. This article also addresses various issues including when to personalize, the evaluation of PIR, privacy, usability, etc. Based on the extensive review, challenges are discussed and directions for future effort are suggested.

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Cited By

Wang HCai TCheng DLi KLin GWu Z(2024)A Web Data Mining Algorithm Based on Manifold Distance for Mixed Data in Cloud Service ArchitectureInternational Journal of Cognitive Informatics and Natural Intelligence10.4018/IJCINI.34402118:1(1-7)Online publication date: 21-Jun-2024
https://dl.acm.org/doi/10.4018/IJCINI.344021
Shah CBender E(2024)Envisioning Information Access Systems: What Makes for Good Tools and a Healthy Web?ACM Transactions on the Web10.1145/364946818:3(1-24)Online publication date: 26-Feb-2024
https://dl.acm.org/doi/10.1145/3649468
Chen ZJiang ZYang FHe ZHou YCho EMcAuley JGalstyan AHu XYang JFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)The First Workshop on Personalized Generative AI @ CIKM 2023: Personalization Meets Large Language ModelsProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615314(5267-5270)Online publication date: 21-Oct-2023
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Index Terms

Personalization in text information retrieval: A survey
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

Index terms have been assigned to the content through auto-classification.

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cover image Journal of the Association for Information Science and Technology

Journal of the Association for Information Science and Technology Volume 71, Issue 3

March 2020

128 pages

ISSN:2330-1635

EISSN:2330-1643

DOI:10.1002/asi.v71.3

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© 2019 ASIS&T.

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John Wiley & Sons, Inc.

United States

Publication History

Published: 28 January 2020

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Cited By

Wang HCai TCheng DLi KLin GWu Z(2024)A Web Data Mining Algorithm Based on Manifold Distance for Mixed Data in Cloud Service ArchitectureInternational Journal of Cognitive Informatics and Natural Intelligence10.4018/IJCINI.34402118:1(1-7)Online publication date: 21-Jun-2024
https://dl.acm.org/doi/10.4018/IJCINI.344021
Shah CBender E(2024)Envisioning Information Access Systems: What Makes for Good Tools and a Healthy Web?ACM Transactions on the Web10.1145/364946818:3(1-24)Online publication date: 26-Feb-2024
https://dl.acm.org/doi/10.1145/3649468
Chen ZJiang ZYang FHe ZHou YCho EMcAuley JGalstyan AHu XYang JFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)The First Workshop on Personalized Generative AI @ CIKM 2023: Personalization Meets Large Language ModelsProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615314(5267-5270)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615314
Lian RLu SSolomon CAguilar GPonnusamy PHan JMa CGuo CChen HDuh WHuang HKato MMothe JPoblete B(2023)PersonalTM: Transformer Memory for Personalized RetrievalProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3592037(2256-2260)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3592037
Ghafourian YHanbury AKnoth P(2023)Ranking for Learning: Studying Users’ Perceptions of Relevance, Understandability, and EngagementLinking Theory and Practice of Digital Libraries10.1007/978-3-031-43849-3_25(284-291)Online publication date: 26-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-43849-3_25
Steichen BKosasih WBecerra CElsweiler DKruschwitz ULudwig B(2022)Inferring Search User Language Proficiency from Eye Gaze DataProceedings of the 2022 Conference on Human Information Interaction and Retrieval10.1145/3498366.3505771(211-220)Online publication date: 14-Mar-2022
https://dl.acm.org/doi/10.1145/3498366.3505771
Lopatovska IKorshakova EBrown DLi YMin JPasiak AZheng KScholer FThomas PElsweiler DJoho HKando NSmith C(2021)User Perceptions of an Intelligent Personal Assistant's PersonalityProceedings of the 2021 Conference on Human Information Interaction and Retrieval10.1145/3406522.3446018(15-25)Online publication date: 14-Mar-2021
https://dl.acm.org/doi/10.1145/3406522.3446018
Liu J(2021)Deconstructing search tasks in interactive information retrievalInformation Processing and Management: an International Journal10.1016/j.ipm.2021.10252258:3Online publication date: 1-May-2021
https://dl.acm.org/doi/10.1016/j.ipm.2021.102522

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