Premium
Uncovering smartphone usage patterns with multi‐view mixed membership models
Author(s) -
Virtanen Seppo,
Rost Mattias,
Morrison Alistair,
Chalmers Matthew,
Girolami Mark
Publication year - 2016
Publication title -
stat
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.61
H-Index - 18
ISSN - 2049-1573
DOI - 10.1002/sta4.103
Subject(s) - computer science , class (philosophy) , data science , smartphone app , data mining , machine learning , artificial intelligence , world wide web
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. © 2016 The Authors. Stat Published by John Wiley & Sons Ltd