Quest for relevant tags using local interaction networks and visual content

Neela Sawant, Ritendra Datta, Jia Li, James Z. Wang

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

20 Scopus citations

Abstract

Typical tag recommendation systems for photos shared on social networks such as Flickr, use visual content analysis, collaborative filtering or personalization strategies to produce annotations. However, the dependence on manual intervention and the knowledge of sufficient personal preferences coupled with the folksonomic issues limit the scope of these strategies. In this paper, we present a fully automatic and folksonomically scalable tag recommendation model that can recommend tags for a user's photos without an explicit knowledge of the user's personal tagging preferences. The model is learned using the collective tagging behavior of other users in the user's local interaction network, which we believe approximates the user's preferences, at least partially. The tag recommendation model generates content-based annotations and then uses a Nave Bayes formulation to translate these annotations to a set of folksonomic tags selected from the tags used by the users in the local interaction network. Quantitative and qualitative comparisons with 890 Flickr networks show that this approach is highly useful for tag recommendation in the presence of insufficient information of a user's own preferences.

Original languageEnglish (US)
Title of host publicationMIR 2010 - Proceedings of the 2010 ACM SIGMM International Conference on Multimedia Information Retrieval
Pages231-240
Number of pages10
DOIs
StatePublished - 2010
Event2010 ACM SIGMM International Conference on Multimedia Information Retrieval, MIR 2010 - Philadelphia, PA, United States
Duration: Mar 29 2010Mar 31 2010

Publication series

NameMIR 2010 - Proceedings of the 2010 ACM SIGMM International Conference on Multimedia Information Retrieval

Other

Other2010 ACM SIGMM International Conference on Multimedia Information Retrieval, MIR 2010
CountryUnited States
CityPhiladelphia, PA
Period3/29/103/31/10

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Information Systems

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