iTag: Incentive-based tagging

In social tagging systems, such as Delicious 1 and Flickr 2 , users are allowed to annotate resources (e.g., Web URLs and images) with textual descriptions called tags. Tags have proven to be invaluable building blocks in algorithms for searching, mining and recommending resources. In practice, howe...

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Bibliographic Details
Published in2014 IEEE 30th International Conference on Data Engineering pp. 1186 - 1189
Main Authors Siyu Lei, Yang, Xuan S., Luyi Mo, Maniu, Silviu, Cheng, Reynold
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2014
Subjects
Collaboration
Facebook
Monitoring
Noise measurement
Real-time systems
Resource management
Tagging
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Summary:In social tagging systems, such as Delicious 1 and Flickr 2 , users are allowed to annotate resources (e.g., Web URLs and images) with textual descriptions called tags. Tags have proven to be invaluable building blocks in algorithms for searching, mining and recommending resources. In practice, however, not all resources receive the same attention from users, and as a result, most tags are added to the few highly-popular resources, while most of the resources receive few tags. Crucially, this incomplete tagging on resources can severely affect the effectiveness of all tagging applications. We present iTag, an incentive-based tagging system, which aims at improving tagging quality of resources, by incentivizing taggers under budget constraints. Our system is built upon traditional crowdsourcing systems such as Amazon Mechanical Turk (MTurk). In our demonstration, we will show how our system allows users to use simple but powerful strategies to significantly improve the tagging quality of resources.
ISSN:1063-6382
2375-026X
DOI:10.1109/ICDE.2014.6816737