Paper Info
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.
Year
DOI
Venue
2010
10.1145/1772690.1772825
WWW
Keywords
Field
DocType
social network,larger social network,sndocrank method return,relevant document,document owner,larger local community,ranking framework,tf-idf algorithm,document content,search result,ranking,social networks,information retrieval
Data mining,World Wide Web,Social network,Ranking,Information retrieval,Computer science,Artificial intelligence,Machine learning
Conference
Citations 
PageRank 
References 
5
0.45
6
Authors
5
Name
Order
Citations
PageRank
Liang Gou126915.49
Hung-Hsuan Chen224616.71
Jung-Hyun Kim3443.27
Xiaolong Zhang427821.91
C. Lee Giles5111541549.48