Probabilistic User Behavior Models

9 years 21 days ago
Probabilistic User Behavior Models
We present a mixture model based approach for learning individualized behavior models for the Web users. We investigate the use of maximum entropy and Markov mixture models for generating probabilistic behavior models. We first build a global behavior model for the entire population and then personalize this global model for the existing users by assigning each user individual component weights for the mixture model. We then use these individual weights to group the users into behavior model clusters. We show that the clusters generated in this manner are interpretable and able to represent dominant behavior patterns. We conduct offline experiments on around two months worth of data from CiteSeer, an online digital library for computer science research papers currently storing more than 470,000 documents. We show that both maximum entropy and Markov based personal user behavior models are strong predictive models. We also show that maximum entropy based mixture model outperforms Mar...
Eren Manavoglu, Dmitry Pavlov, C. Lee Giles
Added 04 Jul 2010
Updated 04 Jul 2010
Type Conference
Year 2003
Where ICDM
Authors Eren Manavoglu, Dmitry Pavlov, C. Lee Giles
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