poster

Modeling facial expressions and peripheral physiological signals to predict topical relevance

Authors:

Ioannis Arapakis,

Ioannis Konstas,

Joemon M. Jose,

Ioannis KompatsiarisAuthors Info & Claims

SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval

Pages 728 - 729

https://doi.org/10.1145/1571941.1572099

Published: 19 July 2009 Publication History

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Abstract

By analyzing explicit & implicit feedback information retrieval systems can determine topical relevance and tailor search criteria to the user's needs. In this paper we investigate whether it is possible to infer what is relevant by observing user affective behaviour. The sensory data employed range between facial expressions and peripheral physiological signals. We extract a set of features from the signals and analyze the data using classification methods, such as SVM and KNN. The results of our initial evaluation indicate that prediction of relevance is possible, to a certain extent, and implicit feedback models can benefit from taking into account user affective behavior.

References

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Cited By

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González‐Ibáñez REsparza‐Villamán AVargas‐Godoy JShah C(2019)A comparison of unimodal and multimodal models for implicit detection of relevance in interactive IRJournal of the Association for Information Science and Technology10.1002/asi.24202Online publication date: 5-Apr-2019
https://doi.org/10.1002/asi.24202
González-Ibáñez REscobar-Macaya MManriquez M(2016)Using low-cost electroencephalography (EEG) sensor to identify perceived relevance on web searchProceedings of the 79th ASIS&T Annual Meeting: Creating Knowledge, Enhancing Lives through Information & Technology10.5555/3017447.3017593(1-5)Online publication date: 14-Oct-2016
https://dl.acm.org/doi/10.5555/3017447.3017593
González‐Ibáñez REscobar‐Macaya MManriquez M(2016)Using low‐cost electroencephalography (EEG) sensor to identify perceived relevance on web searchProceedings of the Association for Information Science and Technology10.1002/pra2.2016.1450530114653:1(1-5)Online publication date: 27-Dec-2016
https://dl.acm.org/doi/10.1002/pra2.2016.14505301146

Index Terms

Modeling facial expressions and peripheral physiological signals to predict topical relevance
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Relevance assessment

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Information & Contributors

Information

Published In

SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval

July 2009

896 pages

ISBN:9781605584836

DOI:10.1145/1571941

General Chairs:
James Allan
University of Massachusetts Amherst, USA
,
Javed Aslam
Northeastern University, USA
,
Program Chairs:
Mark Sanderson
University of Sheffield, UK
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Justin Zobel
University of Melbourne, Australia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 July 2009

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SIGIR '09

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SIGIR '09: The 32nd International ACM SIGIR conference on research and development in Information Retrieval

July 19 - 23, 2009

MA, Boston, USA

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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González‐Ibáñez REsparza‐Villamán AVargas‐Godoy JShah C(2019)A comparison of unimodal and multimodal models for implicit detection of relevance in interactive IRJournal of the Association for Information Science and Technology10.1002/asi.24202Online publication date: 5-Apr-2019
https://doi.org/10.1002/asi.24202
González-Ibáñez REscobar-Macaya MManriquez M(2016)Using low-cost electroencephalography (EEG) sensor to identify perceived relevance on web searchProceedings of the 79th ASIS&T Annual Meeting: Creating Knowledge, Enhancing Lives through Information & Technology10.5555/3017447.3017593(1-5)Online publication date: 14-Oct-2016
https://dl.acm.org/doi/10.5555/3017447.3017593
González‐Ibáñez REscobar‐Macaya MManriquez M(2016)Using low‐cost electroencephalography (EEG) sensor to identify perceived relevance on web searchProceedings of the Association for Information Science and Technology10.1002/pra2.2016.1450530114653:1(1-5)Online publication date: 27-Dec-2016
https://dl.acm.org/doi/10.1002/pra2.2016.14505301146

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A comparison of general vs personalised affective models for the prediction of topical relevance

Using facial expressions and peripheral physiological signals as implicit indicators of topical relevance

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