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.
@phdthesis{saeedi2018thesis,
year = {2018},
author = {Saeedi, Ardavan},
title = {Latent variable models for understanding user behavior in software applicationsg},
month = may,
school = {Massachusetts Institute of Technology},
address = {Cambridge, MA},
keywords = {Electrical Engineering and Computer Science}
}