Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Differences between novice and experienced users in searching information on the World Wide Web
Journal of the American Society for Information Science - Special topic issue: individual differences in virtual environments
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Proceedings of the 15th international conference on World Wide Web
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SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
User Modeling and User-Adapted Interaction
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Analysis of the Publication Sharing Behaviour in BibSonomy
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IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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One of the characteristics of tag prediction mechanisms is that, typically, all user models are constructed with the same granularity. In this paper we hypothesize and empirically demonstrate that in order to increase tag prediction accuracy, the granularity of each user model has to be adapted to the level of usage of each particular user. We have constructed user models for tag prediction using association rules in Bibsonomy, a popular social bookmark and publication sharing system, at three granularity levels: (1) canonical, (2) stereotypical and (3) individual. Our experiments show that prediction accuracy improves if the level of granularity matches the level of participation of the user in the community (i.e., amount of tagging in Bibsonomy).