Indirect Keyword Recommendation

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)


Helping users to find useful contacts or potentially interesting subjects is a challenge for social and productive networks. The evidence of the content produced by users must be considered in this task, which may be simplified by the use of the meta-data associated with the content, i.e., The categorization supported by the network -- descriptive keywords, or tags. In this paper we present a model that enables keyword discovery methods through the interpretation of the network as a graph, solely relying on keywords that categorize or describe productive items. The model and keyword discovery methods presented in this paper avoid content analysis, and move towards a generic approach to the identification of relevant interests and, eventually, contacts. The evaluation of the model and methods is executed by two experiments that perform frequency and classification analyses over the Flickr network. The results show that we can efficiently recommend keywords to users.
Original languageEnglish
Title of host publicationIEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)
PublisherIEEE Computer Society
ISBN (Electronic)978-1-4799-4143-8
ISBN (Print)978-1-4799-4143-8
Publication statusPublished - Jun 2014
EventThe 2014 IEEE/WIC/ACM International Conference on Web Intelligence - Warsaw, Warsaw, Poland
Duration: 11 Aug 201414 Aug 2014


ConferenceThe 2014 IEEE/WIC/ACM International Conference on Web Intelligence
Abbreviated titleWIC 2014
Internet address


  • Context
  • Social Network Services
  • Feature extraction
  • Training
  • Analytical models
  • Production
  • Collaborative work


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