Mining social tags to predict mashup patterns

Khaled Goarany, Gregory Kulczycki, M. Brian Blake

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Citations (Scopus)

Abstract

In the past few years, tagging has gained large momentum as a user-driven approach for categorizing and indexing content on the Web. Mashups have recently joined the list of Web resources targeted for social tagging. In the context of the social Web, a mashup is a lightweight technique for integrating applications and data over the Web. Crafting new mashups is largely a subjective process motivated by the users' initial inspiration. In this paper, we propose a tag-based approach for predicting mashup patterns, thus deriving inspiration for potential new mashups from the community's consensus. Our approach applies association rule mining techniques to discover relationships between APIs and mashups based on their annotated tags. We also advocate the importance of the mined relationships as a valuable source for recommending mashup candidates while mitigating for common problems in recommender systems. We evaluate our methodology through experimentation using real-life dataset. Our results show that our approach achieves high prediction accuracy and outperforms a direct string matching approach that lacks the mining information.

Original languageEnglish
Title of host publicationInternational Conference on Information and Knowledge Management, Proceedings
Pages71-77
Number of pages7
DOIs
StatePublished - Dec 1 2010
Externally publishedYes
Event2nd International Workshop on Search and Mining User-Generated Contents, SMUC'10, Co-located with 19th International Conference on Information and Knowledge Management, CIKM'10 - Toronto, ON, Canada
Duration: Oct 26 2010Oct 30 2010

Other

Other2nd International Workshop on Search and Mining User-Generated Contents, SMUC'10, Co-located with 19th International Conference on Information and Knowledge Management, CIKM'10
CountryCanada
CityToronto, ON
Period10/26/1010/30/10

Fingerprint

Mashup
Tag
World Wide Web
Mashups
Tagging
Prediction accuracy
Social tagging
Experimentation
Resources
Recommender systems
Methodology
Momentum
Indexing
Association rule mining

Keywords

  • Mashup
  • Social tags
  • User-generated content
  • Web mining

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

Cite this

Goarany, K., Kulczycki, G., & Blake, M. B. (2010). Mining social tags to predict mashup patterns. In International Conference on Information and Knowledge Management, Proceedings (pp. 71-77) https://doi.org/10.1145/1871985.1871998

Mining social tags to predict mashup patterns. / Goarany, Khaled; Kulczycki, Gregory; Blake, M. Brian.

International Conference on Information and Knowledge Management, Proceedings. 2010. p. 71-77.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Goarany, K, Kulczycki, G & Blake, MB 2010, Mining social tags to predict mashup patterns. in International Conference on Information and Knowledge Management, Proceedings. pp. 71-77, 2nd International Workshop on Search and Mining User-Generated Contents, SMUC'10, Co-located with 19th International Conference on Information and Knowledge Management, CIKM'10, Toronto, ON, Canada, 10/26/10. https://doi.org/10.1145/1871985.1871998
Goarany K, Kulczycki G, Blake MB. Mining social tags to predict mashup patterns. In International Conference on Information and Knowledge Management, Proceedings. 2010. p. 71-77 https://doi.org/10.1145/1871985.1871998
Goarany, Khaled ; Kulczycki, Gregory ; Blake, M. Brian. / Mining social tags to predict mashup patterns. International Conference on Information and Knowledge Management, Proceedings. 2010. pp. 71-77
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