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Authors: Ludovico Boratto and Salvatore Carta

Affiliation: Università di Cagliari, Italy

ISBN: 978-989-758-028-4

Keyword(s): Group Recommendation, Clustering, Curse of Dimensionality, Collaborative Filtering, Prediction Accuracy.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Collaborative Computing ; Data Engineering ; Enterprise Information Systems ; Health Information Systems ; Information Systems Analysis and Specification ; Knowledge Discovery and Information Retrieval ; Knowledge Management ; Knowledge-Based Systems ; Ontologies and the Semantic Web ; Society, e-Business and e-Government ; Software Agents and Internet Computing ; Symbolic Systems ; User Profiling and Recommender Systems ; Web Information Systems and Technologies

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 th e group recommendations strongly increases with respect to a system that works on sparse data. (More)

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Paper citation in several formats:
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

@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},
}

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

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