loading
Papers

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Olatz Arbelaitz ; Ibai Gurrutxaga ; Aizea Lojo ; Javier Muguerza ; Jesús M. Pérez and Iñigo Perona

Affiliation: University of the Basque Country UPV-EHU, Spain

ISBN: 978-989-8565-29-7

Keyword(s): Adaptive Web, Link Prediction, User Profile, Collaborative Filtering, Machine Learning, Performance Analysis.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Collaborative Filtering ; Computational Intelligence ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems ; User Profiling and Recommender Systems ; Web Mining

Abstract: There is a need to facilitate access to the required information in the web and adapting it to the users’ preferences and requirements. This paper presents a system that, based on a collaborative filtering approach, adapts the web site to improve the browsing experience of the user: it generates automatically interesting links for new users. The system only uses the web log files stored in any web server (common log format) and builds user profiles from them combining machine learning techniques with a generalization process for data representation. These profiles are later used in an exploitation stage to automatically propose links to new users. The paper examines the effect of the parameters of the system on its final performance. Experiments show that the designed system performs efficiently in a database accessible from the web and that the use of a generalization process, specificity in profiles and the use of frequent pattern mining techniques benefit the profile generation pha se, and, moreover, diversity seems to help in the exploitation phase. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.233.217.242

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Arbelaitz, O.; Gurrutxaga, I.; Lojo, A.; Muguerza, J.; M. Pérez, J. and Perona, I. (2012). Adaptation of the User Navigation Scheme using Clustering and Frequent Pattern Mining Techiques for Profiling.In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012) ISBN 978-989-8565-29-7, pages 187-192. DOI: 10.5220/0004130801870192

@conference{kdir12,
author={Olatz Arbelaitz. and Ibai Gurrutxaga. and Aizea Lojo. and Javier Muguerza. and Jesús M. Pérez. and Iñigo Perona.},
title={Adaptation of the User Navigation Scheme using Clustering and Frequent Pattern Mining Techiques for Profiling},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)},
year={2012},
pages={187-192},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004130801870192},
isbn={978-989-8565-29-7},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2012)
TI - Adaptation of the User Navigation Scheme using Clustering and Frequent Pattern Mining Techiques for Profiling
SN - 978-989-8565-29-7
AU - Arbelaitz, O.
AU - Gurrutxaga, I.
AU - Lojo, A.
AU - Muguerza, J.
AU - M. Pérez, J.
AU - Perona, I.
PY - 2012
SP - 187
EP - 192
DO - 10.5220/0004130801870192

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.