Algorithms and strategies for crowdsourcing systems
Abstract/Contents
- Abstract
- Humans are more effective than computers for many tasks, such as identifying concepts in images, translating natural language, and evaluating the usefulness of products. Thus, there has been a lot of recent interest in crowdsourcing, where tasks are outsourced to a distributed group of people. Our goal is to optimize human computation, i.e., to use minimal resources (money, time), while getting high enough quality of results. In this context, we focus on several problems: (a) point-of-interest ranking, (b) summarizing query results, (c) retrieving the maximum item from a set when the comparators are humans, and (d) managing the variance in human worker accuracy. Overall, this thesis provides analysis, solutions, and experiments in real crowdsourcing systems for the aforementioned problems.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2013 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Venetis, Petros |
|---|---|
| Associated with | Stanford University, Computer Science Department |
| Primary advisor | Garcia-Molina, Hector |
| Thesis advisor | Garcia-Molina, Hector |
| Thesis advisor | Leskovec, Jurij |
| Thesis advisor | Polyzotis, Neoklis |
| Advisor | Leskovec, Jurij |
| Advisor | Polyzotis, Neoklis |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Petros Venetis. |
|---|---|
| Note | Submitted to the Department of Computer Science. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2013. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2013 by Petros Venetis
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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