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Authors: Nabil Belacel ; Guillaume Durand ; Serge Leger and Cajetan Bouchard

Affiliation: National Research Council, Canada

Keyword(s): Information Filtering, Recommender Systems, Collaborative Filtering, Clustering, Splitting-merging Clustering.

Related Ontology Subjects/Areas/Topics: Agents ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Computational Intelligence ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Knowledge-Based Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems ; Web Information Systems and Technologies ; Web Intelligence

Abstract: Collaborative filtering (CF) is a well-known and successful filtering technique that has its own limits, especially in dealing with highly sparse and large-scale data. To address this scalability issue, some researchers propose to use clustering methods like K-means that has the shortcomings of having its performances highly dependent on the manual definition of its number of clusters and on the selection of the initial centroids, which leads in case of ill-defined values to inaccurate recommendations and an increase in computation time. In this paper, we will show how the Merging and Splitting clustering algorithm can improve the performances of recommendation with reasonable computation time by comparing it with K-means based approach. Our experiment results demonstrate that the performances of our system are independent on the initial partition by considering the statistical nature of data. More specially, results in this paper provide significant evidences that the propo sed splitting-merging clustering based CF is more scalable than the well-known K-means clustering based CF. (More)

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Paper citation in several formats:
Belacel, N.; Durand, G.; Leger, S. and Bouchard, C. (2018). Splitting-merging Clustering Algorithm for Collaborative Filtering Recommendation System. In Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-275-2; ISSN 2184-433X, SciTePress, pages 165-174. DOI: 10.5220/0006599501650174

@conference{icaart18,
author={Nabil Belacel. and Guillaume Durand. and Serge Leger. and Cajetan Bouchard.},
title={Splitting-merging Clustering Algorithm for Collaborative Filtering Recommendation System},
booktitle={Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2018},
pages={165-174},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006599501650174},
isbn={978-989-758-275-2},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Splitting-merging Clustering Algorithm for Collaborative Filtering Recommendation System
SN - 978-989-758-275-2
IS - 2184-433X
AU - Belacel, N.
AU - Durand, G.
AU - Leger, S.
AU - Bouchard, C.
PY - 2018
SP - 165
EP - 174
DO - 10.5220/0006599501650174
PB - SciTePress