GiveALink: mining a semantic network of bookmarks for web search and recommendation
Proceedings of the 3rd international workshop on Link discovery
Visualizing social links in exploratory search
Proceedings of the nineteenth ACM conference on Hypertext and hypermedia
Evaluating similarity measures for emergent semantics of social tagging
Proceedings of the 18th international conference on World wide web
Proceedings of the 5th International Workshop on Adversarial Information Retrieval on the Web
Incentives for social annotation
Proceedings of the 20th ACM conference on Hypertext and hypermedia
A scalable, collaborative similarity measure for social annotation systems
Proceedings of the 20th ACM conference on Hypertext and hypermedia
Incentives for social annotation
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Bookmark hierarchies and collaborative recommendation
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Harvesting models from web 2.0 databases
Software and Systems Modeling (SoSyM)
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Social bookmarking systems allow Web users to actively annotate online resources. These annotations incorporate meta-information with Web pages in addition to the actual document contents. From a collection of socially annotated resources, we present various methods for quantifying the relationship between objects, i.e., tags or resources. These relationships can then be represented in a semantic similarity network where the nodes represent objects and the undirected weighted edges represent their relations. These relations are quantied through similarity measures. There are two challenges associated with assembling and maintaining such a similarity network. The first challenge is updating the relations efficiently, i.e., the time and space complexity associated with graph algorithms. The complexity of these algorithms is typically quadratic. We present an incremental process answering both space and time limitations. The second challenge is the quality of the similarity measure. We evaluate various measures through the approximation of reference similarities. We then present a number of applications leveraging socially induced semantic similarity networks. A tag recommendation system, a page recommendation engine, and a Web navigation tool are evaluated through user studies. Finally, we design spam detection algorithms to enhance the functionality of social bookmarking systems.