TY - GEN
T1 - Social network document ranking
AU - Gou, Liang
AU - Zhang, Xiaolong
AU - Chen, Hung Hsuan
AU - Kim, Jung Hyun
AU - Giles, C. Lee
PY - 2010
Y1 - 2010
N2 - In search engines, ranking algorithms measure the importance and relevance of documents mainly based on the contents and relationships between documents. User attributes are usually not considered in ranking. This user-neutral approach, however, may not meet the diverse interests of users, who may demand different documents even with the same queries. To satisfy this need for more personalized ranking, we propose a ranking framework, Social Network Document Rank (SNDocRank), that considers both document contents and the relationship between a searcher and document owners in a social network. This method combines the traditional tf-idf ranking for document contents with our Multi-level Actor Similarity (MAS) algorithm to measure to what extent document owners and the searcher are structurally similar in a social network. We implemented our ranking method in a simulated video social network based on data extracted from YouTube and tested its effectiveness on video search. The results show that compared with the traditional ranking method like tf-idf, the SNDocRank algorithm returns more relevant documents. More specifically, a searcher can get significantly better results by being in a larger social network, having more friends, and being associated with larger local communities in a social network.
AB - In search engines, ranking algorithms measure the importance and relevance of documents mainly based on the contents and relationships between documents. User attributes are usually not considered in ranking. This user-neutral approach, however, may not meet the diverse interests of users, who may demand different documents even with the same queries. To satisfy this need for more personalized ranking, we propose a ranking framework, Social Network Document Rank (SNDocRank), that considers both document contents and the relationship between a searcher and document owners in a social network. This method combines the traditional tf-idf ranking for document contents with our Multi-level Actor Similarity (MAS) algorithm to measure to what extent document owners and the searcher are structurally similar in a social network. We implemented our ranking method in a simulated video social network based on data extracted from YouTube and tested its effectiveness on video search. The results show that compared with the traditional ranking method like tf-idf, the SNDocRank algorithm returns more relevant documents. More specifically, a searcher can get significantly better results by being in a larger social network, having more friends, and being associated with larger local communities in a social network.
UR - http://www.scopus.com/inward/record.url?scp=77955112273&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77955112273&partnerID=8YFLogxK
U2 - 10.1145/1816123.1816170
DO - 10.1145/1816123.1816170
M3 - Conference contribution
AN - SCOPUS:77955112273
SN - 9781450300858
T3 - Proceedings of the ACM International Conference on Digital Libraries
SP - 313
EP - 322
BT - JCDL'10 - Digital Libraries - 10 Years Past, 10 Years Forward, a 2020 Vision
T2 - 10th Annual Joint Conference on Digital Libraries, JCDL 2010
Y2 - 21 June 2010 through 25 June 2010
ER -