Personalized Social Search Based on Agglomerative Hierarchical Graph Clustering

Abstract

This paper describes a personalized social search algorithm for retrieving multimedia contents of a consumer generated media (CGM) site having a social network service (SNS). The proposed algorithm generates cluster information on users in the social network by using an agglomerative hierarchical graph clustering, and stores it to a contents database (DB). Retrieved contents are sorted by scores calculated according to similarities of cluster information between a searcher and authors of contents. This paper also describes the evaluation experiments to confirm effectiveness of the proposed algorithm by implementing the proposed algorithm in a video retrieval system of a CGM site.