Automatic classification of social tags
ECDL'10 Proceedings of the 14th European conference on Research and advanced technology for digital libraries
A web search-centric approach to recommender systems with URLs as minimal user contexts
Journal of Systems and Software
Improving tag-based recommendation by topic diversification
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
A supervised machine learning link prediction approach for tag recommendation
OCSC'11 Proceedings of the 4th international conference on Online communities and social computing
Improving neighborhood based Collaborative Filtering via integrated folksonomy information
Pattern Recognition Letters
Using inferred tag ratings to improve user-based collaborative filtering
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Using the overlapping community structure of a network of tags to improve text clustering
Proceedings of the 23rd ACM conference on Hypertext and social media
A social inverted index for social-tagging-based information retrieval
Journal of Information Science
ICWE'12 Proceedings of the 12th international conference on Web Engineering
A novel user-based collaborative filtering method by inferring tag ratings
ACM SIGAPP Applied Computing Review
A performance evaluation of gradient field HOG descriptor for sketch based image retrieval
Computer Vision and Image Understanding
Tag recommendation for open source software
Frontiers of Computer Science: Selected Publications from Chinese Universities
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Tagging with free form tags is becoming an increasingly important indexing mechanism. However, free form tags have characteristics that require special treatment when used for searching or recommendation because they show much more variation than controlled keywords. In this paper we present a method that puts this large variation to good use. We introduce second order co-occurrence and a related distance measure measure for tag similarities that is robust against the variation in tags. From this distance measure it is straightforward to derive methods to analyze user interest and compute recommendations. We evaluate the use of tag based recommendation on the Movielens dataset and a dataset of tagged books.