Latent variable models for understanding user behavior in software applications
Name
1036987746-MIT.pdf
Description
Full printable version
Size
20.46 MB
Format
Adobe PDF
Checksum (MD5)
2516fa0ca04f9bf4e69c1b15ac390d6f
Author(s)
Saeedi, Ardavan
Advisor(s)
Joshua B. Tenenbaum and Ryan P. Adams.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
Understanding user behavior in software applications is of significant interest to software developers and companies. By having a better understanding of the user needs and usage patterns, the developers can design a more efficient workflow, add new features, or even automate the user's workflow. In this thesis, I propose novel latent variable models to understand, predict and eventually automate the user interaction with a software application. I start by analyzing users' clicks using time series models; I introduce models and inference algorithms for time series segmentation which are scalable to large-scale user datasets. Next, using a conditional variational autoencoder and some related models, I introduce a framework for automating the user interaction with a software application. I focus on photo enhancement applications, but this framework can be applied to any domain where segmentation, prediction and personalization is valuable. Finally, by combining sequential Monte Carlo and variational inference, I propose a new inference scheme which has better convergence properties than other reasonable baselines.
Description
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 147-157).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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