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Authors: Rabaa Alabdulrahman 1 ; Herna Viktor 1 and Eric Paquet 2

Affiliations: 1 School of Electirical Engineering and Computer Science, University of Ottawa, Ottawa and Canada ; 2 School of Electirical Engineering and Computer Science, University of Ottawa, Ottawa, Canada, National Research Council of Canada, Ottawa and Canada

ISBN: 978-989-758-330-8

Keyword(s): Recommendation Systems, Hybrid Model, Data Sparsity, Cluster Analysis, Classification, Learning.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Clustering and Classification Methods ; Data Mining in Electronic Commerce ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Symbolic Systems ; User Profiling and Recommender Systems

Abstract: Recommendation systems have a wide application in e-business and have been successful in guiding users in their online purchases. The use of data mining techniques, to aid recommendation systems in their goal to learn the correct user profiles, is an active area of research. In most recent works, recommendations are obtained by applying a supervised learning method, notably the k-nearest neighbour (k-NN) algorithm. However, classification algorithms require a class label, and in many applications, such labels are not available, leading to extensive domain expert labelling. In addition, recommendation systems suffer from a data sparsity problem, i.e. the number of items purchased by a customer is typically a small subset of all ĉvailable products. One solution to overcome the labelling and data sparsity problems is to apply cluster analysis techniques prior to classification. Cluster analysis allows one to learn the natural groupings, i.e. similar customer profiles. In this paper, we s tudy the value of applying cluster analysis techniques to customer ratings prior to applying classification models. Our HCC-Learn framework combines content-based analysis in the cluster analysis stage, with collaborative filtering in the recommending stage. Our experimental results show the value of combining cluster analysis and classification against two real-world data sets. (More)

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Paper citation in several formats:
Alabdulrahman, R.; Viktor, H. and Paquet, E. (2018). Beyond k-NN: Combining Cluster Analysis and Classification for Recommender Systems.In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, ISBN 978-989-758-330-8, pages 82-91. DOI: 10.5220/0006931200820091

@conference{kdir18,
author={Rabaa Alabdulrahman. and Herna Viktor. and Eric Paquet.},
title={Beyond k-NN: Combining Cluster Analysis and Classification for Recommender Systems},
booktitle={Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,},
year={2018},
pages={82-91},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006931200820091},
isbn={978-989-758-330-8},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,
TI - Beyond k-NN: Combining Cluster Analysis and Classification for Recommender Systems
SN - 978-989-758-330-8
AU - Alabdulrahman, R.
AU - Viktor, H.
AU - Paquet, E.
PY - 2018
SP - 82
EP - 91
DO - 10.5220/0006931200820091

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