Session Boundary Detection for Association Rule Learning Using n-Gram Language Models
- Xiangji Huang, School of Computer Science, University of Waterloo
- Fuchun Peng, Department of Computer Science, University of Massachusetts at Amherst
- Aijun An, Department of Computer Science, York University
- Dale Schuurmans, AICML
- Nick Cercone, School of Computer Science, University of Waterloo
We present a statistical method using n-gram language mod- els to identify session boundaries in a large collection of Livelink log data. The identi ed sessions are then used for association rule learning. Unlike the traditional ad hoc timeout method, which uses xed time thresh- olds for session identi cation, our method uses an information theoretic approach that provides a natural technique for performing dynamic ses- sion identi cation. The e ectiveness of our approach is evaluated with respect to 4 di erent interestingness measures. We nd that we obtain a signi cant improvement in each interestingness measure, ranging from a 26.6% to 39% improvement on average over the best results obtained with standard timeout methods.
Citation
X. Huang, F. Peng, A. An, D. Schuurmans, N. Cercone. "Session Boundary Detection for Association Rule Learning Using n-Gram Language Models". Canadian Conference on Artificial Intelligence (CAI), Halifax, Nova Scotia, Canada, January 2003.| Keywords: | web usage mining, language modeling, evaluation, machine learning |
| Category: | In Conference |
BibTeX
@incollection{Huang+al:CAI03,
author = {Xiangji Huang and Fuchun Peng and Aijun An and Dale Schuurmans and
Nick Cercone},
title = {Session Boundary Detection for Association Rule Learning Using
n-Gram Language Models},
booktitle = {Canadian Conference on Artificial Intelligence (CAI)},
year = 2003,
}Last Updated: June 01, 2007Submitted by Staurt H. Johnson
