Maintaining state transition data for a plurality of users,...

Details Maintaining state transition data for a plurality of users,... Maintaining state transition data for a plurality of users,...

2011-05-10

2011-05-10

Vincent, David R (Department: 2129)

Data processing: artificial intelligence

Neural network

Learning task

Reexamination Certificate

active

07941383

ABSTRACT:
Mechanisms model, detect, and predict user behavior as a user navigates the Web. In one embodiment, mechanisms model user behavior using predictive models, such as discrete Markov processes, where the user's behavior transitions between a finite number of states. The user's behavior state may not be directly observable (e.g., a user does not proactively indicate what behavior state he is in). Thus, the behavior state of a user is usually only indirectly observable. Mechanisms use predictive models, such as hidden Markov models, to predict the transitions in the user's behavior states.

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Deshpande et al., Selective Markov MOdels for Predicting Web PAge Accesses, 2004, ACM Transactions on Internet Technology, vol. 4. No. 2, pp. 163-184.
“Hidden Markov model” downloaded from the Internet on Feb. 26, 2008 < http://en.wikipedia.org/wiki/Hidden—Markov—model > 5 pages.
“Hidden Markov Models” downloaded from the Internet on Feb. 26, 2008 < http://www.cs.brown.edu/research/ai/dynamics/tutorial/Documents/HiddenMarkovModels.html > 6 pages.

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