Quest for Relevant Tags using Local Interaction Networks and Visual Content

Neela Sawant, Ritendra Datta, Jia Li and James Z. Wang
The Pennsylvania State University, University Park, PA

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, constraints on the scalability, manual intervention, sufficient personal preferences and other folksonomic issues limit the applicability 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 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 Naive Bayes formulation to translate these annotations to a set of folksonomic tags selected from the tags used by 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 user's own preferences.

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Citation: Neela Sawant, Ritendra Datta, Jia Li and James Z. Wang, ``Quest for Relevant Tags using Local Interaction Networks and Visual Content,'' Proceedings of the ACM International Conference on Multimedia Information Retrieval, Special Session on Statistical Modeling and Learning for Multimedia, pp. 231-240, Philadelphia, Pennsylvania, ACM, March 2010.

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Last Modified: January 26, 2010
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