Social network document ranking

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 sh...

• Type: Conference
• Year: 2010
• Where: JCDL
• Authors: Liang Gou, Xiaolong Zhang, Hung-Hsuan Chen, Jung-Hyun Kim, C. Lee Giles
• Keywords: Document, Education, JCDL 2010, Ranking, Social Network