Latent variable model estimation via collaborative filtering
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
| Published: |
Massachusetts Institute of Technology
2018
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/113932 |
| _version_ | 1811078076166045696 |
|---|---|
| author | Lee, Christina (Christina Esther) |
| author2 | Asuman Ozdaglar and Devavrat Shah. |
| author_facet | Asuman Ozdaglar and Devavrat Shah. Lee, Christina (Christina Esther) |
| author_sort | Lee, Christina (Christina Esther) |
| collection | MIT |
| description | Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. |
| first_indexed | 2024-09-23T10:53:00Z |
| format | Thesis |
| id | mit-1721.1/113932 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T10:53:00Z |
| publishDate | 2018 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1139322019-04-12T17:39:26Z Latent variable model estimation via collaborative filtering Lee, Christina (Christina Esther) Asuman Ozdaglar and Devavrat Shah. 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, 2017. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 255-264). Similarity based collaborative filtering for matrix completion is a popular heuristic that has been used widely across industry in the previous decades to build recommendation systems, due to its simplicity and scalability. However, despite its popularity, there has been little theoretical foundation explaining its widespread success. In this thesis, we prove theoretical guarantees for collaborative filtering under a nonparametric latent variable model, which arises from the natural property of "exchangeability", i.e. invariance under relabeling of the dataset. The analysis suggests that similarity based collaborative filtering can be viewed as kernel regression for latent variable models, where the features are not directly observed and the kernel must be estimated from the data. In addition, while classical collaborative filtering typically requires a dense dataset, this thesis proposes a new collaborative filtering algorithm which compares larger radius neighborhoods of data to compute similarities, and show that the estimate converges even for very sparse datasets, which has implications towards sparse graphon estimation. The algorithms can be applied in a variety of settings, such as recommendations for online markets, analysis of social networks, or denoising crowdsourced labels. by Christina E. Lee. Ph. D. 2018-03-02T21:39:48Z 2018-03-02T21:39:48Z 2017 2017 Thesis http://hdl.handle.net/1721.1/113932 1023861300 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 264 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Lee, Christina (Christina Esther) Latent variable model estimation via collaborative filtering |
| title | Latent variable model estimation via collaborative filtering |
| title_full | Latent variable model estimation via collaborative filtering |
| title_fullStr | Latent variable model estimation via collaborative filtering |
| title_full_unstemmed | Latent variable model estimation via collaborative filtering |
| title_short | Latent variable model estimation via collaborative filtering |
| title_sort | latent variable model estimation via collaborative filtering |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/113932 |
| work_keys_str_mv | AT leechristinachristinaesther latentvariablemodelestimationviacollaborativefiltering |