GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fab: content-based, collaborative recommendation
Communications of the ACM
Information archiving with bookmarks: personal Web space construction and organization
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
A multi-agent system for collaborative bookmarking
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
Exploiting hierarchical domain structure to compute similarity
ACM Transactions on Information Systems (TOIS)
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An Empirical Analysis of Web Page Revisitation
HICSS '01 Proceedings of the 34th Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 5 - Volume 5
Algorithmic detection of semantic similarity
WWW '05 Proceedings of the 14th international conference on World Wide Web
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Visualizing social links in exploratory search
Proceedings of the nineteenth ACM conference on Hypertext and hypermedia
Ranking Web Pages from User Perspectives of Social Bookmarking Sites
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Constructing folksonomies from user-specified relations on flickr
Proceedings of the 18th international conference on World wide web
Improving Document Search Using Social Bookmarking
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
“Socially induced semantic networks and applications” by Benjamin Markines
ACM SIGWEB Newsletter
Dynamic adaptation strategies for long-term and short-term user profile to personalize search
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Prediction of social bookmarking based on a behavior transition model
Proceedings of the 2010 ACM Symposium on Applied Computing
GiveALink tagging game: an incentive for social annotation
Proceedings of the ACM SIGKDD Workshop on Human Computation
A privacy-aware architecture for a web rating system
IBM Journal of Research and Development
The decreasing marginal value of evaluation network size
ACM SIGCAS Computers and Society
The chain model for social tagging game design
Proceedings of the 6th International Conference on Foundations of Digital Games
PeerSec: towards peer production and crowdsourcing for enhanced security
HotSec'12 Proceedings of the 7th USENIX conference on Hot Topics in Security
Emergent semantics from game-induced folksonomies
Proceedings of the First International Workshop on Crowdsourcing and Data Mining
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GiveALink.org is a social bookmarking site where users may donate and view their personal bookmark files online securely. The bookmarks are analyzed to build a new generation of intelligent information retrieval techniques to recommend, search, and personalize the Web. GiveALink does not use tags, content, or links in the submitted Web pages. Instead we present a semantic similarity measure for URLs that takes advantage both of the hierarchical structure in the bookmark files of individual users, and of collaborative filtering across users. In addition, we build a recommendation and search engine from ranking algorithms based on popularity and novelty measures extracted from the similarity-induced network. Search results can be personalized using the bookmarks submitted by a user. We evaluate a subset of the proposed ranking measures by conducting a study with human subjects.