Virtanen, S., Rost, M., Morrison, A., Chalmers, M. and Girolami, M. (2016) Uncovering smartphone usage patterns with multi-view mixed membership models. Stat, 5(1), pp. 57-69. (doi: 10.1002/sta4.103)
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Abstract
We present a novel class of mixed membership models for combining information from multiple data sources inferring inter-view and intra-view statistical associations. An important contemporary application of this work is the meaningful synthesis of data sources corresponding to smartphone application usage, app developers' descriptions and customer feedback. We demonstrate the ability of the model to infer meaningful, interpretable and informative app usage patterns based on the app usage data augmented with rich text data describing the apps. We provide quantitative model evaluations showing the model provides significantly better predictive ability than comparative related existing methods.
Item Type: | Articles |
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Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Morrison, Dr Alistair and Chalmers, Professor Matthew and Rost, Dr Mattias and Girolami, Prof Mark |
Authors: | Virtanen, S., Rost, M., Morrison, A., Chalmers, M., and Girolami, M. |
College/School: | College of Science and Engineering > School of Computing Science |
Journal Name: | Stat |
Publisher: | John Wiley & Sons, Ltd. |
ISSN: | 2049-1573 |
ISSN (Online): | 2049-1573 |
Copyright Holders: | Copyright © 2016 The Authors |
First Published: | First published in Stat 5(1):57-69 |
Publisher Policy: | Reproduced under a Creative Commons License |
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