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.
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Nico Nico Douga. http://www.nicovideo.jp. Accessed 12 Sept 2018
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Ishizuka, K. (2018). Personalized Social Search Based on Agglomerative Hierarchical Graph Clustering. In: Tseng, YH., et al. Information Retrieval Technology. AIRS 2018. Lecture Notes in Computer Science(), vol 11292. Springer, Cham. https://doi.org/10.1007/978-3-030-03520-4_4
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DOI: https://doi.org/10.1007/978-3-030-03520-4_4
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Online ISBN: 978-3-030-03520-4
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