The reasons why users tag have remained mostly elusive to quantitative investigations. In this paper, we distinguish between two types of motivation for tagging: While categorizers use tags mainly for categorizing resources for later browsing, describers use tags mainly for describing resources for later retrieval. To characterize users with regard to these different motivations, we introduce statistical measures and apply them to 7 different real-world tagging datasets. We show that while most taggers use tags for both categorizing and describing resources, different tagging systems lend themselves to different motivations for tagging. Additionally we show that the distinction between describers and categorizers can improve the performance of tag recommendation.
- Synthetic Dataset
- Conditional Entropy
- Personal Information Management
- Comet Program
- Optimal Encode Strategy
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