A scalable, collaborative similarity measure for social annotation systems

Authors:
Benjamin Markines;Filippo Menczer
Affiliations:
Indiana University, Bloomington, IN, USA;Indiana University, Bloomington, IN, USA
Venue:
Proceedings of the 20th ACM conference on Hypertext and hypermedia
Year:
2009

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Abstract

Collaborative annotation tools are in widespread use. The metadata from these systems can be mined to induce semantic relationships among Web objects (sites, pages, tags, concepts, users), which in turn can support improved search, recommendation, and otherWeb applications. We build upon prior work by extracting relationships among tags and among resources from two social bookmarking systems, Bibsonomy.org and GiveALink.org. We introduce a scalable and collaborative measure that we name maximum information path (MIP) similarity. Our analysis shows that MIP outperforms the best scalable similarity measures in the literature. We are currently integrating MIP similarity into a number of applications under development in the GiveALink project, including search and recommendation, Web navigation maps, bookmark management, social networks, spam detection, and a tagging game to create incentives for collaborative annotations.