Flickr group recommendation based on quaternary semantic analysis

Flickr groups are self-organized user communities revolving around a common interest and of massive popularity. In recent years, the explosive growth in the number of the group makes it difficult for Flickr users to find relevant groups they are really interested in. In this paper, we focus on automatically recommending groups to users. Many existing works have utilized only ternary relationship such as users-tags-groups to generate their recommendations. In our work, we show that ternary relationship is insufficient to provide accurate recommendations. Instead, we represent the quaternary relationship among users, tags, image clusters and groups as a 4-order tensor and further employ the Higher-Order Singular Value Decomposition to reduce the dimensionality of the 4-order tensor; accordingly the group recommendation problem is casted as a latent semantic analysis problem between users and groups. Experiments on the dataset crawled from Flickr and comparisons with the ternary relationship model demonstrate the effectiveness of the proposed approach. Copyright © 2013 Binary Information Press.