An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Introduction to Information Retrieval
Introduction to Information Retrieval
Tag Recommendations in Folksonomies
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Context-based ranking in folksonomies
Proceedings of the 20th ACM conference on Hypertext and hypermedia
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Proceedings of the 20th ACM conference on Hypertext and hypermedia
Collaborative Semantic Tagging of Web Resources on the Basis of Individual Knowledge Networks
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Personalized social search based on the user's social network
Proceedings of the 18th ACM conference on Information and knowledge management
Folksonomies. Indexing and Retrieval in Web 2.0
Folksonomies. Indexing and Retrieval in Web 2.0
The social bookmark and publication management system bibsonomy
The VLDB Journal — The International Journal on Very Large Data Bases
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
The impact of multifaceted tagging on learning tag relations and search
ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part II
FReSET: an evaluation framework for folksonomy-based recommender systems
Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web
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By means of tagging in social bookmarking applications, so called folksonomies emerge collaboratively. Folksonomies have shown to contain information that is beneficial for resource recommendation. However, as folksonomies are not designed to support recommendation tasks, there are drawbacks of the various recommendation techniques. Graph-based recommendation in folksonomies for example suffers from the problem of concept drift. Vector space based recommendation approaches in folksonomies suffer from sparseness of available data. In this paper, we propose the flexible framework VSScore which incorporates context-specific information into the recommendation process to tackle these issues. Additionally, as an alternative to the evaluation methodology LeavePostOut we propose an adaptation LeaveRTOut for resource recommendation in folksonomies. In a subset of resource recommendation tasks evaluated, the proposed recommendation framework VSScore performs significantly more effective than the baseline algorithm FolkRank.