Probabilistic user behavior models

Eren Manavoglu, Dmitry Pavlov, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

68 Citations (SciVal)


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.

Original languageEnglish (US)
Title of host publicationProceedings - 3rd IEEE International Conference on Data Mining, ICDM 2003
Number of pages8
StatePublished - 2003
Event3rd IEEE International Conference on Data Mining, ICDM '03 - Melbourne, FL, United States
Duration: Nov 19 2003Nov 22 2003

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Other3rd IEEE International Conference on Data Mining, ICDM '03
Country/TerritoryUnited States
CityMelbourne, FL

All Science Journal Classification (ASJC) codes

  • Engineering(all)


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