Social network document ranking

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

研究成果: 書貢獻/報告類型會議論文篇章同行評審

20 引文 斯高帕斯(Scopus)


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.

主出版物標題JCDL'10 - Digital Libraries - 10 Years Past, 10 Years Forward, a 2020 Vision
出版狀態已出版 - 2010
事件10th Annual Joint Conference on Digital Libraries, JCDL 2010 - Gold Coast, QLD, Australia
持續時間: 21 6月 201025 6月 2010


名字Proceedings of the ACM International Conference on Digital Libraries


???event.eventtypes.event.conference???10th Annual Joint Conference on Digital Libraries, JCDL 2010
城市Gold Coast, QLD


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