research-article

Surprise Me If You Can: Serendipity in Health Information

Authors:

Mary Lou Maher,

Kazjon GraceAuthors Info & Claims

CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

Paper No.: 23, Pages 1 - 12

https://doi.org/10.1145/3173574.3173597

Published: 19 April 2018 Publication History

Abstract

Our natural tendency to be curious is increasingly important now that we are exposed to vast amounts of information. We often cope with this overload by focusing on the familiar: information that matches our expectations. In this paper we present a framework for interactive serendipitous information discovery based on a computational model of surprise. This framework delivers information that users were not actively looking for, but which will be valuable to their unexpressed needs. We hypothesize that users will be surprised when presented with information that violates the expectations predicted by our model of them. This surprise model is balanced by a value component which ensures that the information is relevant to the user. Within this framework we have implemented two surprise models, one based on association mining and the other on topic modeling approaches. We evaluate these two models with thirty users in the context of online health news recommendation. Positive user feedback was obtained for both of the computational models of surprise compared to a baseline random method. This research contributes to the understanding of serendipity and how to "engineer" serendipity that is favored by users.

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Cited By

Fu ZNiu XWu XRahman R(2024)A Deep Learning Model for Cross-Domain Serendipity RecommendationsACM Transactions on Recommender Systems10.1145/3690654Online publication date: 29-Aug-2024
https://doi.org/10.1145/3690654
Bauer CBagchi CHundogan Ovan Es K(2024)Where Are the Values? A Systematic Literature Review on News Recommender SystemsACM Transactions on Recommender Systems10.1145/36548052:3(1-40)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3654805
Fu ZNiu X(2023)Modeling Users’ Curiosity in Recommender SystemsACM Transactions on Knowledge Discovery from Data10.1145/361759818:1(1-23)Online publication date: 16-Oct-2023
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cover image ACM Conferences

CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

April 2018

8489 pages

ISBN:9781450356206

DOI:10.1145/3173574

General Chairs:
Regan Mandryk
University of Saskatchewan, Canada
,
Mark Hancock
University of Waterloo, Canada
,
Program Chairs:
Mark Perry
Brunel University London, UK
,
Anna Cox
University College London, UK

Copyright © 2018 ACM.

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Published: 19 April 2018

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CHI '18: CHI Conference on Human Factors in Computing Systems

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CHI '18 Paper Acceptance Rate 666 of 2,590 submissions, 26%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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Cited By

Fu ZNiu XWu XRahman R(2024)A Deep Learning Model for Cross-Domain Serendipity RecommendationsACM Transactions on Recommender Systems10.1145/3690654Online publication date: 29-Aug-2024
https://doi.org/10.1145/3690654
Bauer CBagchi CHundogan Ovan Es K(2024)Where Are the Values? A Systematic Literature Review on News Recommender SystemsACM Transactions on Recommender Systems10.1145/36548052:3(1-40)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3654805
Fu ZNiu X(2023)Modeling Users’ Curiosity in Recommender SystemsACM Transactions on Knowledge Discovery from Data10.1145/361759818:1(1-23)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3617598
Fu ZNiu XMaher M(2023)Deep Learning Models for Serendipity Recommendations: A Survey and New PerspectivesACM Computing Surveys10.1145/360514556:1(1-26)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3605145
Hasan TBunescu R(2023)Topic-Level Bayesian Surprise and Serendipity for Recommender SystemsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608851(933-939)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608851
Maccatrozzo VKuhn TCeolin DVan Ossenbruggen J(2023)The Role of Serendipity in User-Curated Music PlaylistsProceedings of the 12th Knowledge Capture Conference 202310.1145/3587259.3627552(140-147)Online publication date: 5-Dec-2023
https://dl.acm.org/doi/10.1145/3587259.3627552
Mohseni MMaher M(2022)A Framework for Exploring Computational Models of Novelty in Unstructured TextProceedings of the 6th International Conference on Information System and Data Mining10.1145/3546157.3546164(36-45)Online publication date: 27-May-2022
https://dl.acm.org/doi/10.1145/3546157.3546164
Ko HAn SPark GKim SKim DKim BJo JSeo J(2022)We-toon: A Communication Support System between Writers and Artists in Collaborative Webtoon Sketch RevisionProceedings of the 35th Annual ACM Symposium on User Interface Software and Technology10.1145/3526113.3545612(1-14)Online publication date: 29-Oct-2022
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Grace KFinch EGulbransen-Diaz NHenderson H(2022)Q-Chef: The impact of surprise-eliciting systems on food-related decision-makingProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3501862(1-14)Online publication date: 29-Apr-2022
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Wu XNiu XRahman RAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Topological Analysis of Contradictions in TextProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531881(2478-2483)Online publication date: 6-Jul-2022
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