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
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, 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.
Copyright 2010 ACM.
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Last Modified:
January 26, 2010
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