document: A probabilistic model for personalized tag prediction Social tagging systems have become increasingly popular for sharing and organizing web resources . Tag prediction is a common feature of social tagging systems . Social tagging by nature is an incremental process , meaning that once a user has saved a web page with tags , the tagging system can provide more accurate predictions for the user , based on user 's incremental behaviors . However , existing tag prediction methods do not consider this important factor , in which their training and test datasets are either split by a fixed time stamp or randomly sampled from a larger corpus . In our temporal experiments , we perform a time-sensitive sampling on an existing public dataset , resulting in a new scenario which is much closer to `` real-world '' . In this paper , we address the problem of tag prediction by proposing a probabilistic model for personalized tag prediction . The model is a Bayesian approach , and integrates three factors - ego-centric effect , environmental effects and web page content . Two methods - both intuitive calculation and learning optimization - are provided for parameter estimation . Pure graphbased methods which may have significant constraints ( such as every user , every item and every tag has to occur in at least p posts ) , can not make a prediction in most of `` real world '' cases while our model improves the F-measure by over 30 % compared to a leading algorithm , in our `` real-world '' use case .
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