- Mohammad Akbari20,21,
- Alberto Cetoli21,
- Stefano Bragaglia21,
- Andrew D. O’Harney21,
- Marc Sloan21 &
- …
- Jun Wang20
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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Authors and Affiliations
University College London, London, UK
Mohammad Akbari & Jun Wang
Context Scout, London, UK
Mohammad Akbari, Alberto Cetoli, Stefano Bragaglia, Andrew D. O’Harney & Marc Sloan
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Correspondence toMohammad Akbari.
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University of Strathclyde, Glasgow, UK
Leif Azzopardi
Bauhaus Universität Weimar, Weimar, Germany
Benno Stein
Universität Duisburg-Essen, Duisburg, Germany
Norbert Fuhr
GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany
Philipp Mayr
Delft University of Technology, Delft, The Netherlands
Claudia Hauff
University of Twente, Enschede, The Netherlands
Djoerd Hiemstra
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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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