Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Log Mining to Improve the Performance of Site Search
WISEW '02 Proceedings of the Third International Conference on Web Information Systems Engineering (Workshops) - (WISEw'02)
Optimizing web search using web click-through data
Proceedings of the thirteenth ACM international conference on Information and knowledge management
The Influence of Indirect Association Rules on Recommendation Ranking Lists
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
HT06, tagging paper, taxonomy, Flickr, academic article, to read
Proceedings of the seventeenth conference on Hypertext and hypermedia
Optimizing web search using social annotations
Proceedings of the 16th international conference on World Wide Web
VIPAS: virtual link powered authority search in the web
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Bookmark hierarchies and collaborative recommendation
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
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Recently, the growth of social bookmark sites (e.g.,\\del.icio.us) brings a new way to organize and share web pages. Specially, the social bookmarking sites contain many bookmarks of users, and users, who bookmark web pages, would frequently browse these pages in the future. Therefore, we argue that social bookmarking sites provide the readers' perspective and are able to take the perspective into consideration in ranking web pages. In this paper, we propose two ranking algorithms, ExpertVoteRank and RecommendationPageRank, to reveal the diverse information of web pages in the social bookmarking sites. The concept of both algorithms are based on the views of readers: ExpertVoteRank takes advantage of experts of readers, while RecommendationPageRank applies recommendations from crowds to web pages. Note that we collected about 90 millions data. Experiments show that both algorithms have effectiveness to rank web pages according to the viewpoint of users.