TWO PRUNING METHODS FOR ONLINE PPM WEB PAGE PREDICTION

Alborz Moghaddam, Ehsanollah kabir

Abstract

The Web access prediction gets significant attention in recent years. Web prefetching and some personalization systems use prediction algorithms. Most current applications that predict the next web page have an offline part that does the data preparation task and an online part that provides personalized content to the users based on their current navigational activities. We use PPM for modelling user navigation history. In standard PPM, many states of the model are rarely useful for prediction and can be eliminated without affecting the performance of the model. In this paper we propose two pruning methods. Using these methods we present an online prediction model that fits in the memory with good prediction accuracy. A performance evaluation is presented using real web logs. This evaluation shows that our methods effectively decrease the memory complexity.

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Paper Citation


in Harvard Style

Moghaddam A. and kabir E. (2009). TWO PRUNING METHODS FOR ONLINE PPM WEB PAGE PREDICTION . In - WEBIST, ISBN , pages 0-0


in Bibtex Style

@conference{webist09,
author={Alborz Moghaddam and Ehsanollah kabir},
title={TWO PRUNING METHODS FOR ONLINE PPM WEB PAGE PREDICTION},
booktitle={ - WEBIST,},
year={2009},
pages={},
publisher={SciTePress},
organization={INSTICC},
doi={},
isbn={},
}


in EndNote Style

TY - CONF
JO - - WEBIST,
TI - TWO PRUNING METHODS FOR ONLINE PPM WEB PAGE PREDICTION
SN -
AU - Moghaddam A.
AU - kabir E.
PY - 2009
SP - 0
EP - 0
DO -