TY - GEN
T1 - Quest for relevant tags using local interaction networks and visual content
AU - Sawant, Neela
AU - Datta, Ritendra
AU - Li, Jia
AU - Wang, James Z.
N1 - Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=77952338762&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77952338762&partnerID=8YFLogxK
U2 - 10.1145/1743384.1743424
DO - 10.1145/1743384.1743424
M3 - Conference contribution
AN - SCOPUS:77952338762
SN - 9781605588155
T3 - MIR 2010 - Proceedings of the 2010 ACM SIGMM International Conference on Multimedia Information Retrieval
SP - 231
EP - 240
BT - MIR 2010 - Proceedings of the 2010 ACM SIGMM International Conference on Multimedia Information Retrieval
T2 - 2010 ACM SIGMM International Conference on Multimedia Information Retrieval, MIR 2010
Y2 - 29 March 2010 through 31 March 2010
ER -