Paper Info
Title
Flickr group recommendation based on user-generated tags and social relations via topic model
Abstract
The boom of Flickr, a photo-sharing social tagging system, leads to a dramatical increasing of online social interactions. For example, it offers millions of groups for users to join in order to share photos and keep relations. However, the rapidly increasing amount of groups hampers users' participation, thus it is necessary to suggest groups according to users' preferences. By analyzing user-generated tags, one can explore users' potential interests, and discover the latent topics of the corresponding groups. Furthermore, users' behaviors are affected by their friends. Based on these intuitions, we propose a topic-based group recommendation model to predict users' potential interests and conduct group recommendations based on tags and social relations. The proposed model provides a way to fuse tag information and social network structure to predict users' future interests accurately. The experimental results on a real-world dataset demonstrate the effectiveness of the proposed model.
Year
DOI
Venue
2013
10.1007/978-3-642-39068-5_62
ISNN (2)
Keywords
Field
DocType
corresponding group,flickr group recommendation,photo-sharing social tagging system,online social interaction,social relation,topic-based group recommendation model,conduct group recommendation,potential interest,topic model,user-generated tag,social network structure
Social relation,World Wide Web,Social network,Information retrieval,Computer science,Intuition,Topic model,Future interests,Boom,Recommendation model,Tag system
Conference
Citations 
PageRank 
References 
4
0.39
18
Authors
2
Name
Order
Citations
PageRank
Nan Zheng140.39
Hongyun Bao2484.23