JSM 2005 Online Program

Abstract #304229

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	363
Type:	Contributed
Date/Time:	Wednesday, August 10, 2005 : 8:30 AM to 10:20 AM
Sponsor:	Section on Bayesian Statistical Science
Abstract - #304229
Title:	A Doubly Nested Hidden Markov Model for Internet Browsing Behavior
Author(s):	Steven Scott*+
Companies:	University of Southern California
Address:	Bridge Hall 401H, Los Angeles, CA, 90089, United States
Keywords:	hidden Markov model ; forward-backward recursion ; internet traffic modeling ; click stream
Abstract:	Consumers shopping in an online store generate "click stream" data that online retailers would like to use to personalize the customer's online shopping experience. The click stream data has an interesting temporal structure in which sequences of individual page requests are nested within browsing sessions, and some users return to the web site for multiple sessions. We model sequences of page requests within a session using a hidden Markov mixture of first-order Markov chains. Variation across customers (and sessions within a customer) is captured by assuming the model parameters describing the page requests within a session belong to a collection of such parameters allocated to individual sessions by a second latent Markov chain. Efficient Markov chain Monte Carlo analysis of this model is made possible by forward-backward recursions that allow both latent Markov chains to be simulated directly from their joint posterior distribution.

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