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Modeling User Return Time Using Inhomogeneous Poisson Process

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Part of the book series:Lecture Notes in Computer Science ((LNISA,volume 11438))

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Abstract

For Intelligent Assistants (IA), user activity is often used as a lag metric for user satisfaction or engagement. Conversely, predictive leading metrics for engagement can be helpful with decision making and evaluating changes in satisfaction caused by new features. In this paper, we propose User Return Time (URT), a fine grain metric for gauging user engagement. To compute URT, we model continuous inter-arrival times between users’ use of service via a log Gaussian Cox process (LGCP), a form of inhomogeneous Poisson process which captures the irregular variations in user usage rate and personal preferences typical of an IA. We show the effectiveness of the proposed approaches on predicting the return time of users on real-world data collected from an IA. Experimental results demonstrate that our model is able to predict user return times reasonably well and considerably better than strong baselines that make the prediction based on past utterance frequency.

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Author information

Authors and Affiliations

  1. University College London, London, UK

    Mohammad Akbari & Jun Wang

  2. Context Scout, London, UK

    Mohammad Akbari, Alberto Cetoli, Stefano Bragaglia, Andrew D. O’Harney & Marc Sloan

Authors
  1. Mohammad Akbari

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  2. Alberto Cetoli

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  3. Stefano Bragaglia

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  4. Andrew D. O’Harney

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  5. Marc Sloan

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  6. Jun Wang

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Corresponding author

Correspondence toMohammad Akbari.

Editor information

Editors and Affiliations

  1. University of Strathclyde, Glasgow, UK

    Leif Azzopardi

  2. Bauhaus Universität Weimar, Weimar, Germany

    Benno Stein

  3. Universität Duisburg-Essen, Duisburg, Germany

    Norbert Fuhr

  4. GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany

    Philipp Mayr

  5. Delft University of Technology, Delft, The Netherlands

    Claudia Hauff

  6. University of Twente, Enschede, The Netherlands

    Djoerd Hiemstra

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Cite this paper

Akbari, M., Cetoli, A., Bragaglia, S., O’Harney, A.D., Sloan, M., Wang, J. (2019). Modeling User Return Time Using Inhomogeneous Poisson Process. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds) Advances in Information Retrieval. ECIR 2019. Lecture Notes in Computer Science(), vol 11438. Springer, Cham. https://doi.org/10.1007/978-3-030-15719-7_5

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Price includes VAT (Japan)
  • Available as EPUB and PDF
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JPY 7149
Price includes VAT (Japan)
  • Compact, lightweight edition
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