Content preference estimation in online social networks: Message passing versus sparse reconstruction on graphs

We design two different strategies for computing the unknown content preferences in an online social network based on a small set of nodes in the corresponding social graph for which this information is available ahead of time. The techniques take advantage of the graph’s structure and the additional affinity information between the social contacts, expressed through the graph’s edge weights, to optimize the computation of the missing preference data. The first strategy is distributed and comprises a local computation step and a message passing step that are iteratively applied at each node in the graph, until convergence. We carry out a graph Laplacian based analysis of the performance of the algorithm and verify the analytical findings via numerical experiments involving sample social networks. The second strategy is centralized and involves a sparse transform of the content preference data represented as a function over the nodes of the social graph. We solve the related opt...

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Jacob Chakareski
• Keywords: Content Preference, ICASSP 2011, Signal Processing, Social, Social Graph