Splitting-merging Clustering Algorithm for Collaborative Filtering Recommendation System

Nabil Belacel, Guillaume Durand, Serge Leger, Cajetan Bouchard

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 proposed splitting-merging clustering based CF is more scalable than the well-known K-means clustering based CF.

Download


Paper Citation


in Harvard Style

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 2: ICAART, ISBN 978-989-758-275-2, pages 165-174. DOI: 10.5220/0006599501650174


in Bibtex Style

@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 2: ICAART,},
year={2018},
pages={165-174},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006599501650174},
isbn={978-989-758-275-2},
}


in EndNote Style

TY - CONF

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