Latent variable models for understanding user behavior in software applications
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2018
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| Online Access: | http://hdl.handle.net/1721.1/115779 |
| _version_ | 1826201550314274816 |
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| author | Saeedi, Ardavan |
| author2 | Joshua B. Tenenbaum and Ryan P. Adams. |
| author_facet | Joshua B. Tenenbaum and Ryan P. Adams. Saeedi, Ardavan |
| author_sort | Saeedi, Ardavan |
| collection | MIT |
| description | Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. |
| first_indexed | 2024-09-23T11:53:11Z |
| format | Thesis |
| id | mit-1721.1/115779 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T11:53:11Z |
| publishDate | 2018 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1157792019-04-11T05:29:52Z Latent variable models for understanding user behavior in software applications Saeedi, Ardavan Joshua B. Tenenbaum and Ryan P. Adams. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. 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). 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. by Ardavan Saeedi. Ph. D. 2018-05-23T16:34:28Z 2018-05-23T16:34:28Z 2018 2018 Thesis http://hdl.handle.net/1721.1/115779 1036987746 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 157 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Saeedi, Ardavan Latent variable models for understanding user behavior in software applications |
| title | Latent variable models for understanding user behavior in software applications |
| title_full | Latent variable models for understanding user behavior in software applications |
| title_fullStr | Latent variable models for understanding user behavior in software applications |
| title_full_unstemmed | Latent variable models for understanding user behavior in software applications |
| title_short | Latent variable models for understanding user behavior in software applications |
| title_sort | latent variable models for understanding user behavior in software applications |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/115779 |
| work_keys_str_mv | AT saeediardavan latentvariablemodelsforunderstandinguserbehaviorinsoftwareapplications |