Analysis of the Impact of a Tag Recommendation System in a Real-World Folksonomy

@article{Font2015AnalysisOT,
  title={Analysis of the Impact of a Tag Recommendation System in a Real-World Folksonomy},
  author={Frederic Font and Joan Serr{\`a} and Xavier Serra},
  journal={ACM Transactions on Intelligent Systems and Technology (TIST)},
  year={2015},
  volume={7},
  pages={1 - 27}
}
  • F. Font, J. Serrà, X. Serra
  • Published 22 September 2015
  • Computer Science
  • ACM Transactions on Intelligent Systems and Technology (TIST)
Collaborative tagging systems have emerged as a successful solution for annotating contributed resources to online sharing platforms, facilitating searching, browsing, and organizing their contents. To aid users in the annotation process, several tag recommendation methods have been proposed. It has been repeatedly hypothesized that these methods should contribute to improving annotation quality and reducing the cost of the annotation process. It has been also hypothesized that these methods… 
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References

SHOWING 1-10 OF 56 REFERENCES
Folksonomy-Based Tag Recommendation for Collaborative Tagging Systems
TLDR
The authors present a general scheme for building a folksonomy-based tag recommendation system to help users tagging online content resources and show how novel strategies for selecting the appropriate number of tags to be recommended can significantly improve methods performances.
Tag Recommendations in Folksonomies
TLDR
This paper evaluates and compares two recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering and a graph-based recommender built on top of FolkRank, showing that both provide better results than non-personalized baseline methods.
Testing and evaluating tag recommenders in a live system
TLDR
The tag recommendation framework the authors developed for their social bookmark and publication sharing system BibSonomy is described, designed to be easily extensible, open for a variety of methods, and usable independent from Bib Sonomy.
Class-based tag recommendation and user-based evaluation in online audio clip sharing
Semantic Stability and Implicit Consensus in Social Tagging Streams
TLDR
Tagging streams that are generated by a combination of imitation dynamics and shared background knowledge exhibit faster and higher semantic stability than tagging streams that were generated via imitation dynamics or natural language phenomena alone.
HT06, tagging paper, taxonomy, Flickr, academic article, to read
TLDR
A model of tagging systems, specifically in the context of web-based systems, is offered to help illustrate the possible benefits of these tools and a simple taxonomy of incentives and contribution models is provided to inform potential evaluative frameworks.
Semantic stability in social tagging streams
TLDR
The results show that tagging streams which are generated by a combination of imitation dynamics and shared background knowledge exhibit faster and higher semantic stability than tagging streamsWhich are generated via imitation dynamics or natural language phenomena alone.
Flickr tag recommendation based on collective knowledge
TLDR
This paper analyzes a representative snapshot of Flickr and presents and evaluates tag recommendation strategies to support the user in the photo annotation task by recommending a set of tags that can be added to the photo.
...
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4
5
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