Recommendation systems on social networks
Abstract/Contents
- Abstract
- Recommendation systems have great power to know and shape who we are. First, we discuss how Twitter's feed-recommendation system exposes sensitive and uniquely identifying information, and explain how this knowledge can be used to identify anonymous visitors on social-media sites. Next, we describe a randomized controlled experiment we ran on Twitter's Who-To-Follow recommendation system that drastically influenced the social networks of individual Twitter users. Finally, we use a natural experiment to quantify the effect of the Who-To-Follow system on the global structure of the Twitter follow graph, and discuss its implications for large-scale social dynamics.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2018 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Su, Jessica |
|---|---|
| Associated with | Stanford University, Computer Science Department. |
| Primary advisor | Goel, Sharad, 1977- |
| Primary advisor | Leskovec, Jurij |
| Thesis advisor | Goel, Sharad, 1977- |
| Thesis advisor | Leskovec, Jurij |
| Thesis advisor | Ullman, Jeffrey D, 1942- |
| Advisor | Ullman, Jeffrey D, 1942- |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Jessica Su. |
|---|---|
| Note | Submitted to the Department of Computer Science. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2018. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2018 by Jessica Tsu-Yun Su
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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