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TWO PRUNING METHODS FOR ONLINE PPM WEB PAGE PREDICTION

Topics: Learning User Profiles; User Modeling; Web Personalization

Authors: Alborz Moghaddam and Ehsanollah kabir

Affiliation: Tarbiat Modares University, Iran, Islamic Republic of

Keyword(s): Prediction, Memory optimization, Pruning, PPM.

Related Ontology Subjects/Areas/Topics: Data Engineering ; Ontologies and the Semantic Web ; User Modeling ; Web Information Systems and Technologies ; Web Interfaces and Applications ; Web Personalization

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.

CC BY-NC-ND 4.0

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

@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={},
}

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 -

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