@inproceedings{8dc1edb145f54f71b350ad72aedb1d2d,
title = "SNDocRank: Document ranking based on social networks",
abstract = "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.",
keywords = "information retrieval, multilevel actor similarity, ranking, social networks",
author = "Liang Gou and Chen, {Hung Hsuan} and Kim, {Jung Hyun} and Xiaolong Zhang and Giles, {C. Lee}",
year = "2010",
doi = "10.1145/1772690.1772825",
language = "???core.languages.en_GB???",
isbn = "9781605587998",
series = "Proceedings of the 19th International Conference on World Wide Web, WWW '10",
pages = "1103--1104",
booktitle = "Proceedings of the 19th International Conference on World Wide Web, WWW '10",
note = "19th International World Wide Web Conference, WWW2010 ; Conference date: 26-04-2010 Through 30-04-2010",
}