SNDocRank: Document ranking based on social networks

Liang Gou, Hung Hsuan Chen, Jung Hyun Kim, Xiaolong Zhang, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


To improve the search results for socially-connect users, we propose a ranking framework, Social Network Document Rank (SNDocRank). This framework considers both document contents and the similarity between a searcher and document owners in a social network and uses a Multi-level Actor Similarity (MAS) algorithm to efficiently calculate user similarity in a social network. Our experiment results based on YouTube data show that compared with the tf-idf algorithm, the SNDocRank method returns more relevant documents of interest. Our findings suggest that in this framework, a searcher can improve search by joining larger social networks, having more friends, and connecting larger local communities in a social network.

Original languageEnglish (US)
Title of host publicationProceedings of the 19th International Conference on World Wide Web, WWW '10
Number of pages2
StatePublished - Jul 20 2010
Event19th International World Wide Web Conference, WWW2010 - Raleigh, NC, United States
Duration: Apr 26 2010Apr 30 2010


Other19th International World Wide Web Conference, WWW2010
Country/TerritoryUnited States
CityRaleigh, NC

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications


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