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Abstract

Every online sharing system gathers data that reflects users’ collective behaviour and their shared activities. This data can be used to extract different kinds of relationships, which can be grouped into layers, and which are basic components of the multidimensional social network proposed in the paper. The layers are built based on both types of relations between humans, i.e. direct and object-based ones and correspond to either social or semantic links between individuals. For better understanding of the social network structure complexity, layers and their profiles were identified and studied on two, spanned in time, snapshots of the Flickr population. Additionally, for each layer, a separate strength measure was proposed. The experiments on the Flickr photo sharing system revealed that the relationships between users result either from semantic links between objects they operate on or from social connections to other users. Moreover, the density of the social network increases in time. The second part of the study is devoted to building a social recommender system that supports the creation of new relations between users in a multimedia sharing system. Its main goal is to generate personalized suggestions that are continuously adapted to users’ needs depending on the personal weights assigned to each layer from the multidimensional social network. The conducted experiments confirmed the usefulness of the proposed model.

Available Versions of this Item

• Multidimensional Social Network and Its Application to the Social Recommender System. (deposited 21 Sep 2010 12:04)
  • Multidimensional Social Network and Its Application to the Social Recommender System. (deposited 24 Jun 2011 11:27) [Currently Displayed]