TY - GEN
T1 - Probabilistic user behavior models
AU - Manavoglu, Eren
AU - Pavlov, Dmitry
AU - Giles, C. Lee
PY - 2003
Y1 - 2003
N2 - 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 Markov mixture models in recognizing complex user behavior patterns.
AB - 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 Markov mixture models in recognizing complex user behavior patterns.
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M3 - Conference contribution
AN - SCOPUS:36348987015
SN - 0769519784
SN - 9780769519784
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 203
EP - 210
BT - Proceedings - 3rd IEEE International Conference on Data Mining, ICDM 2003
T2 - 3rd IEEE International Conference on Data Mining, ICDM '03
Y2 - 19 November 2003 through 22 November 2003
ER -