A probabilistic model for personalized tag prediction

Social tagging systems have become increasingly popular for sharing and organizing web resources. Tag recommendation 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 the user’s incremental behavior. 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— an ego-centric effect, environmental effects and w...

• Type: Conference
• Year: 2010
• Where: KDD
• Authors: Dawei Yin, Zhenzhen Xue, Liangjie Hong, Brian D. Davison
• Keywords: Data Mining, KDD 2010, Social Tagging, Social Tagging Systems, Tag Prediction