Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
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
Tag recommendations in social bookmarking systems
AI Communications
Latent dirichlet allocation for tag recommendation
Proceedings of the third ACM conference on Recommender systems
Fast algorithms for determining (generalized) core groups in social networks
Advances in Data Analysis and Classification
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
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Since the rise of collaborative tagging systems on the web, the tag recommendation task -- suggesting suitable tags to users of such systems while they add resources to their collection -- has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used so-called post-cores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a large-scale experiment in which we analyze different tag recommendation algorithms on different cores of three real-world datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores.