Temporal Dynamics of User Interests in Tagging Systems

Collaborative tagging systems are now deployed extensively to help users share and organize resources. Tag prediction and recommendation systems generally model user behavior as research has shown that accuracy can be significantly improved by modeling users’ preferences. However, these preferences are usually treated as constant over time, neglecting the temporal factor within users’ interests. On the other hand, little is known about how this factor may influence prediction in social bookmarking systems. In this paper, we investigate the temporal dynamics of user interests in tagging systems and propose a user-tag-specific temporal interests model for tracking users’ interests over time. Additionally, we analyze the phenomenon of topic switches in social bookmarking systems, showing that a temporal interests model can benefit from the integration of topic switch detection and that temporal characteristics of social tagging systems are different from traditional concept dri...

• Type: Journal
• Year: 2011
• Where: AAAI
• Authors: Dawei Yin, Liangjie Hong, Zhenzhen Xue, Brian D. Davison
• Keywords: AAAI 2011, Concept Drift, Intelligent Agents, Public Datasets, Tag Prediction