research-article

HypTrails: A Bayesian Approach for Comparing Hypotheses About Human Trails on the Web

Authors:

Philipp Singer,

Markus StrohmaierAuthors Info & Claims

WWW '15: Proceedings of the 24th International Conference on World Wide Web

Pages 1003 - 1013

https://doi.org/10.1145/2736277.2741080

Published: 18 May 2015 Publication History

Abstract

When users interact with the Web today, they leave sequential digital trails on a massive scale. Examples of such human trails include Web navigation, sequences of online restaurant reviews, or online music play lists. Understanding the factors that drive the production of these trails can be useful for e.g., improving underlying network structures, predicting user clicks or enhancing recommendations. In this work, we present a general approach called HypTrails for comparing a set of hypotheses about human trails on the Web, where hypotheses represent beliefs about transitions between states. Our approach utilizes Markov chain models with Bayesian inference. The main idea is to incorporate hypotheses as informative Dirichlet priors and to leverage the sensitivity of Bayes factors on the prior for comparing hypotheses with each other. For eliciting Dirichlet priors from hypotheses, we present an adaption of the so-called (trial) roulette method. We demonstrate the general mechanics and applicability of HypTrails by performing experiments with (i) synthetic trails for which we control the mechanisms that have produced them and (ii) empirical trails stemming from different domains including website navigation, business reviews and online music played. Our work expands the repertoire of methods available for studying human trails on the Web.

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Cited By

Koopmann TBecker MLemmerich FHotho A(2024)CompTrails: comparing hypotheses across behavioral networksData Mining and Knowledge Discovery10.1007/s10618-023-00996-838:3(1258-1288)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s10618-023-00996-8
Santos TLemmerich FHelic D(2023)Bayesian estimation of decay parameters in Hawkes processesIntelligent Data Analysis10.3233/IDA-21628327:1(223-240)Online publication date: 30-Jan-2023
https://doi.org/10.3233/IDA-216283
Arora AGerlach MPiccardi TGarcía-Durán AWest RSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)Wikipedia Reader NavigationProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498496(16-26)Online publication date: 11-Feb-2022
https://dl.acm.org/doi/10.1145/3488560.3498496
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Index Terms

HypTrails: A Bayesian Approach for Comparing Hypotheses About Human Trails on the Web
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Web-based interaction

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Published In

cover image ACM Other conferences

WWW '15: Proceedings of the 24th International Conference on World Wide Web

May 2015

1460 pages

ISBN:9781450334693

General Chairs:
Aldo Gangemi
National Research Council, Italy & Paris 13 University-CNRS, France
,
Stefano Leonardi
Sapienza University of Rome, Italy
,
Alessandro Panconesi
Sapienza University of Rome, Italy

Copyright © 2015 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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IW3C2: International World Wide Web Conference Committee

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 18 May 2015

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DFG German Science Fund
FWF Austrian Science Fund

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WWW '15

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IW3C2

WWW '15: 24th International World Wide Web Conference

May 18 - 22, 2015

Florence, Italy

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WWW '15 Paper Acceptance Rate 131 of 929 submissions, 14%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Koopmann TBecker MLemmerich FHotho A(2024)CompTrails: comparing hypotheses across behavioral networksData Mining and Knowledge Discovery10.1007/s10618-023-00996-838:3(1258-1288)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s10618-023-00996-8
Santos TLemmerich FHelic D(2023)Bayesian estimation of decay parameters in Hawkes processesIntelligent Data Analysis10.3233/IDA-21628327:1(223-240)Online publication date: 30-Jan-2023
https://doi.org/10.3233/IDA-216283
Arora AGerlach MPiccardi TGarcía-Durán AWest RSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)Wikipedia Reader NavigationProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498496(16-26)Online publication date: 11-Feb-2022
https://dl.acm.org/doi/10.1145/3488560.3498496
Petrovic LScholtes I(2022)Learning the Markov Order of Paths in GraphsProceedings of the ACM Web Conference 202210.1145/3485447.3512091(1559-1569)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3485447.3512091
Mahajan SChen BKarduni AKim YWall E(2022)VIBE: A Design Space for VIsual Belief Elicitation in Data JournalismComputer Graphics Forum10.1111/cgf.1455641:3(477-488)Online publication date: 12-Aug-2022
https://doi.org/10.1111/cgf.14556
Cifci EGokturk M(2022)Predictivity of Category Based Human Navigation and the Effect of Navigation Path Length on the Prediction Accuracy in Knowledge Networks2022 International Conference on Communications, Information, Electronic and Energy Systems (CIEES)10.1109/CIEES55704.2022.9990641(1-6)Online publication date: 24-Nov-2022
https://doi.org/10.1109/CIEES55704.2022.9990641
Chen HYang CZhang XLiu ZSun MJin J(2021)From Symbols to Embeddings: A Tale of Two Representations in Computational Social ScienceJournal of Social Computing10.23919/JSC.2021.00112:2(103-156)Online publication date: Jun-2021
https://doi.org/10.23919/JSC.2021.0011
Koopmann TStubbemann MKapa MParis MBuenstorf GHanika THotho AJäschke RStumme G(2021)Proximity dimensions and the emergence of collaboration: a HypTrails study on German AI researchScientometrics10.1007/s11192-021-03922-1126:12(9847-9868)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1007/s11192-021-03922-1
Slama AAbad ZFar B(2021)Detection of Asynchronous Concatenation Emergent Behaviour in Multi-Agent SystemsAgents and Multi-Agent Systems: Technologies and Applications 202110.1007/978-981-16-2994-5_7(77-88)Online publication date: 8-Jun-2021
https://doi.org/10.1007/978-981-16-2994-5_7
Zhao PDing ZWang MCao R(2019)Behavior Analysis for Electronic Commerce Trading Systems: A SurveyIEEE Access10.1109/ACCESS.2019.29332477(108703-108728)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2933247
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