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Personalization in text information retrieval: A survey

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  • Jingjing Liu
  • Chang Liu
  • Nicholas J. Belkin

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.

Suggested Citation

  • Jingjing Liu & Chang Liu & Nicholas J. Belkin, 2020. "Personalization in text information retrieval: A survey," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(3), pages 349-369, March.
    • Handle: RePEc:bla:jinfst:v:71:y:2020:i:3:p:349-369
    DOI: 10.1002/asi.24234
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    Cited by:

    1. Hui Wang & Tie Cai & Dongsheng Cheng & Kangshun Li & Guangming Lin & Zhijian Wu, 2024. "A Web Data Mining Algorithm Based on Manifold Distance for Mixed Data in Cloud Service Architecture," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 18(1), pages 1-7, January.
    2. Frans van der Sluis & Egon L. van den Broek, 2023. "Feedback beyond accuracy: Using eye‐tracking to detect comprehensibility and interest during reading," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(1), pages 3-16, January.

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