Quest for relevant tags using local interaction networks and visual content

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 Na¨ıve Bayes formulation to translate these annotations to a set of folksonomic tags selected from the tags used by the users in the l...

• Type: Conference
• Year: 2010
• Where: MIR
• Authors: Neela Sawant, Ritendra Datta, Jia Li, James Ze Wang
• Keywords: Local Interaction Networks, MIR 2010, Multimedia, Tag Recommendation, Tag Recommendation Model