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Personalization is not a panacea: balancing serendipity and personalization in medical news content delivery

Authors:

Xiangyu Fan,

Javed Mostafa,

Ketan Mane,

Cassidy SugimotoAuthors Info & Claims

IHI '12: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium

Pages 709 - 714

https://doi.org/10.1145/2110363.2110445

Published: 28 January 2012 Publication History

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Abstract

Personalization is viewed as a potential solution to the "information overload" problem. In contrast, serendipity is a natural part of human information seeking process that can lead to unexpected and useful discoveries. It appears both serendipity and personalization are important. However, it remains unclear how these modes of interaction impact content retrieval and consumption. To empirically analyze the influence of personalization and serendipity on information retrieval, a medical news information system named MedSIFTER was developed. The system can personalize the presentation of news articles based on users' interest profiles. Using a control variable, built into the system, MeSIFTER's personalization level can be modulated, ranging from high to low (or zero). In this study, based on MedlinePlus as main information source, three different system modalities were compared (zero, low, and high levels of personalization). The experimental analysis engaged three different user groups, over a four week period. Strong evidence of serendipity was found across all three user groups, independent of the level of personalization. Users also appeared to be uniformly satisfied regardless of the level of personalization.

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Cited By

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Chen CHuang EYan H(2018)Detecting the association of health problems in consumer-level medical textJournal of Information Science10.1177/016555151667162944:1(3-14)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1177/0165551516671629
Afridi A(2018)Visualizing Serendipitous Recommendations in User Controlled Recommender System for LearningProcedia Computer Science10.1016/j.procs.2018.10.136141(496-502)Online publication date: 2018
https://doi.org/10.1016/j.procs.2018.10.136
McCay-Peet LToms E(2017)Researching Serendipity in Digital Information EnvironmentsSynthesis Lectures on Information Concepts, Retrieval, and Services10.2200/S00790ED1V01Y201707ICR0599:6(i-91)Online publication date: 28-Sep-2017
https://doi.org/10.2200/S00790ED1V01Y201707ICR059
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Index Terms

Personalization is not a panacea: balancing serendipity and personalization in medical news content delivery
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
    2. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

IHI '12: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium

January 2012

914 pages

ISBN:9781450307819

DOI:10.1145/2110363

General Chairs:
Gang Luo
IBM Research, USA
,
Jiming Liu
Hong Kong Baptist University
,
Program Chair:
Christopher C. Yang
Drexel University

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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Association for Computing Machinery

New York, NY, United States

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Published: 28 January 2012

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IHI '12: ACM International Health Informatics Symposium

January 28 - 30, 2012

Florida, Miami, USA

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Chen CHuang EYan H(2018)Detecting the association of health problems in consumer-level medical textJournal of Information Science10.1177/016555151667162944:1(3-14)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1177/0165551516671629
Afridi A(2018)Visualizing Serendipitous Recommendations in User Controlled Recommender System for LearningProcedia Computer Science10.1016/j.procs.2018.10.136141(496-502)Online publication date: 2018
https://doi.org/10.1016/j.procs.2018.10.136
McCay-Peet LToms E(2017)Researching Serendipity in Digital Information EnvironmentsSynthesis Lectures on Information Concepts, Retrieval, and Services10.2200/S00790ED1V01Y201707ICR0599:6(i-91)Online publication date: 28-Sep-2017
https://doi.org/10.2200/S00790ED1V01Y201707ICR059
Yu HWang YFan YMeng SHuang R(2017)Accuracy Is Not Enough: Serendipity Should Be Considered MoreInnovative Mobile and Internet Services in Ubiquitous Computing10.1007/978-3-319-61542-4_22(231-241)Online publication date: 5-Jul-2017
https://doi.org/10.1007/978-3-319-61542-4_22
Bong CHoltby DNg K(2012)Fuzzy Multicriteria Decision Analysis for Measurement of Document Content ReliabilityProceedings of the 2012 Fifth International Symposium on Computational Intelligence and Design - Volume 0210.1109/ISCID.2012.227(303-306)Online publication date: 28-Oct-2012
https://dl.acm.org/doi/10.1109/ISCID.2012.227

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Personalized recommendation based on the personal innovator degree

Generating semantically enriched user profiles for Web personalization

Investigating serendipity in recommender systems based on real user feedback

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Personalized recommendation based on the personal innovator degree

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