Collaborative tagging systems are now deployed extensivelyto help users share and organize resources.Tag prediction and recommendation systems generallymodel user behavior as research has shown that accuracycan be significantly improved by modeling users’preferences. However, these preferences are usuallytreated as constant over time, neglecting the temporalfactor within users’ interests. On the other hand, littleis known about how this factor may influence predictionin social bookmarking systems. In this paper, weinvestigate the temporal dynamics of user interests intagging systems and propose a user-tag-specific temporalinterests model for tracking users’ interests overtime. Additionally, we analyze the phenomenon of topicswitches in social bookmarking systems, showing that atemporal interests model can benefit from the integrationof topic switch detection and that temporal characteristicsof social tagging systems are different fromtraditional concept drift problems. We conduct experimentson three public datasets, demonstrating the importanceof personalization and user-tag specializationin tagging systems. Experimental results show that ourmethod can outperform state-of-the-art tag predictionalgorithms. We also incorporate our model within existingcontent-based methods yielding significant improvementsin performance.