Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Introduction to Information Retrieval
Introduction to Information Retrieval
Personalized recommendation in social tagging systems using hierarchical clustering
Proceedings of the 2008 ACM conference on Recommender systems
Tag recommendations in social bookmarking systems
AI Communications
Evaluating similarity measures for emergent semantics of social tagging
Proceedings of the 18th international conference on World wide web
Pairwise interaction tensor factorization for personalized tag recommendation
Proceedings of the third ACM international conference on Web search and data mining
Using self-defined group activities for improvingrecommendations in collaborative tagging systems
Proceedings of the fourth ACM conference on Recommender systems
The social bookmark and publication management system bibsonomy
The VLDB Journal — The International Journal on Very Large Data Bases
Improving tag-based recommendation by topic diversification
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Resource recommendation in social annotation systems: A linear-weighted hybrid approach
Journal of Computer and System Sciences
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The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.