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Predicting users’ next access to the web server Chakoula, Oxana
Abstract
The World Wide Web is experiencing a rapid growth both in the volume of traffic and complexity of web sites. Two of the main directions of research on improving users' web browsing experience are reduction of network latency perceived by users, and website personalization. Both tasks require the development of models that can predict a user's next request to a web server. Markov models trained on web logs have been found well suited for addressing this problem. Most of the variations of Markov models suggested for predicting a user's next request to a web server base their predictions on the data from the entire user population. Our hypothesis is that clustering users' web sessions to reflect browsing patterns of particular groups of users will improve prediction accuracy. We consider two clustering techniques that use different representations of web sessions. One approach treats web sessions as Markov models of order zero, and the other represents them as Markov models of order one. We compare the two clustered prediction models to the state-of-the-art non-clustered Markov models of orders 0, 1, All-2 and All-3. We report empirical results based on web logs of UBC Computer Science department. We found clustering web sessions as Markov chains performed better than clustering them of Markov models of order zero, yet the former did not improve predictability over the non-clustered first order Markov model.
Item Metadata
Title |
Predicting users’ next access to the web server
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
2003
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Description |
The World Wide Web is experiencing a rapid growth both in the volume of traffic and complexity of web sites. Two of the main directions of research on improving users' web browsing experience are reduction of network latency perceived by users, and website personalization. Both tasks require the development of models that can predict a user's next request to a web server. Markov models trained on web logs have been found well suited for addressing this problem. Most of the variations of Markov models suggested for predicting a user's next request to a web server base their predictions on the data from the entire user population. Our hypothesis is that clustering users' web sessions to reflect browsing patterns of particular groups of users will improve prediction accuracy. We consider two clustering techniques that use different representations of web sessions. One approach treats web sessions as Markov models of order zero, and the other represents them as Markov models of order one. We compare the two clustered prediction models to the state-of-the-art non-clustered Markov models of orders 0, 1, All-2 and All-3. We report empirical results based on web logs of UBC Computer Science department. We found clustering web sessions as Markov chains performed better than clustering them of Markov models of order zero, yet the former did not improve predictability over the non-clustered first order Markov model.
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Extent |
1419004 bytes
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Genre | |
Type | |
File Format |
application/pdf
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Language |
eng
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Date Available |
2009-10-17
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Provider |
Vancouver : University of British Columbia Library
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Rights |
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.
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DOI |
10.14288/1.0051164
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
2003-05
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Campus | |
Scholarly Level |
Graduate
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Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.