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Multi-view Personality Profiling Based on Longitudinal Data

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Experimental IR Meets Multilinguality, Multimodality, and Interaction (CLEF 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11018))

Abstract

Personality profiling is an essential application for the marketing, advertisement and sales industries. Indeed, the knowledge about one’s personality may help in understanding the reasons behind one’s behavior and his/her motivation in undertaking new life challenges. In this study, we take the first step towards solving the problem of automatic personality profiling. Specifically, we propose the idea of fusing multi-source multi-modal temporal data in our computational “PersonalLSTM” framework for automatic user personality inference. Experimental results show that incorporation of multi-source temporal data allows for more accurate personality profiling, as compared to non-temporal baselines and different data source combinations.

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Notes

  1. 1.

    http://weibo.com.

  2. 2.

    http://dev.twitter.com/rest/public.

  3. 3.

    http://16personalities.com.

  4. 4.

    http://humanmetrics.com/.

  5. 5.

    http://mbtionline.com.

  6. 6.

    We empirically set \(\alpha = 0.5\), \(\beta = 0.1\), \(T = 50\) topics for 1, 000 LDA iterations.

  7. 7.

    Is it possible to improve the performance of user personality profiling by leveraging on temporal data aspect?

  8. 8.

    Which data sources contribute the most to user personality profiling?.

  9. 9.

    Is it possible to perform user personality profiling more accurately by learning from multiple incomplete data sources?.

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Author information

Authors and Affiliations

  1. ITMO University, 49 Kronverksky Pr., St. Petersburg, 197101, Russia

    Kseniya Buraya, Aleksandr Farseev & Andrey Filchenkov

  2. SoMin Research, 221 Henderson Rd., Singapore, 159557, Singapore

    Aleksandr Farseev

Authors
  1. Kseniya Buraya
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  2. Aleksandr Farseev
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  3. Andrey Filchenkov
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Corresponding author

Correspondence to Kseniya Buraya .

Editor information

Editors and Affiliations

  1. Aix-Marseille University, Marseille Cedex 20, France

    Patrice Bellot

  2. Virtual University of Tunis, Tunis, Tunisia

    Chiraz Trabelsi

  3. Systèmes d’informations, Big Data et Rec, Institut de Recherche en Informatique de, Toulouse Cedex 04, France

    Josiane Mothe

  4. Department of Computer Science, University of Huddersfield, Huddersfield, United Kingdom

    Fionn Murtagh

  5. DIRO, Universite de Montreal, Montreal, Québec, Canada

    Jian Yun Nie

  6. Pierre and Marie Curie University, Paris Cedex 05, France

    Laure Soulier

  7. Université d'Avignon et des Pays de, Avignon, France

    Eric SanJuan

  8. Department of Information Engineering, University of Padua, Padua, Padova, Italy

    Linda Cappellato

  9. University of Padua, Padua, Italy

    Nicola Ferro

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Buraya, K., Farseev, A., Filchenkov, A. (2018). Multi-view Personality Profiling Based on Longitudinal Data. In: Bellot, P., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2018. Lecture Notes in Computer Science(), vol 11018. Springer, Cham. https://doi.org/10.1007/978-3-319-98932-7_2

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  • DOI: https://doi.org/10.1007/978-3-319-98932-7_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98931-0

  • Online ISBN: 978-3-319-98932-7

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