Using Collaborative Filtering to Overcome the Curse of Dimensionality when Clustering Users in a Group Recommender System

Ludovico Boratto, Salvatore Carta

Abstract

A characteristic of most datasets is that the number of data points is much lower than the number of dimensions (e.g., the number of movies rated by a user is much lower than the number of movies in a dataset). Dealing with high-dimensional and sparse data leads to problems in the classification process, known as curse of dimensionality. Previous researches presented approaches that produce group recommendations by clustering users in contexts where groups are not available. In the literature it is widely-known that clustering is one of the classification forms affected by the curse of dimensionality. In this paper we propose an approach to remove sparsity from a dataset before clustering users in group recommendation. This is done by using a Collaborative Filtering approach that predicts the missing data points. In such a way, it is possible to overcome the curse of dimensionality and produce better clusterings. Experimental results show that, by removing sparsity, the accuracy of the group recommendations strongly increases with respect to a system that works on sparse data.

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Paper Citation


in Harvard Style

Boratto L. and Carta S. (2014). Using Collaborative Filtering to Overcome the Curse of Dimensionality when Clustering Users in a Group Recommender System . In Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-028-4, pages 564-572. DOI: 10.5220/0004865005640572


in Bibtex Style

@conference{iceis14,
author={Ludovico Boratto and Salvatore Carta},
title={Using Collaborative Filtering to Overcome the Curse of Dimensionality when Clustering Users in a Group Recommender System},
booktitle={Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2014},
pages={564-572},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004865005640572},
isbn={978-989-758-028-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Using Collaborative Filtering to Overcome the Curse of Dimensionality when Clustering Users in a Group Recommender System
SN - 978-989-758-028-4
AU - Boratto L.
AU - Carta S.
PY - 2014
SP - 564
EP - 572
DO - 10.5220/0004865005640572