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Authors: Aristomenis S. Lampropoulos ; Paraskevi S. Lampropoulou and George A. Tsihrintzis

Affiliation: University of Piraeus, Greece

ISBN: 978-989-8425-84-3

Keyword(s): Transductive SVM, Recommender system, Ensemble of classifiers.

Related Ontology Subjects/Areas/Topics: Adaptive Architectures and Mechanisms ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neural Based Data Mining and Complex Information Processing ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Supervised and Unsupervised Learning ; Support Vector Machines and Applications ; Theory and Methods

Abstract: In this paper, we address the recommendation process as a classification problem based on content features and a bank of Transductive SVMclassifiers that capture user preferences. Specifically, we develop an ensemble of Transductive SVM(TSVM) classifiers, each of which utilizes a different feature vector extracted fromdifferent semantic meta-data such as actors, directors, writers, editors and genres. The ensemble classifier allows our system to utilize feature vectors of meta-data from a database and to make personalized recommendations to users. This is achieved through the property of TSVM classifiers to utilize a large amount of available unlabeled data together with a small amount of labeled data that constitute the rated movies of a user. The proposed method is compared to a TSVM classifier which utilizes a feature vector extracted from only ratings of users. The experimental results based on the MovieLens data set indicated that our classifier based on an ensemble of TSVM with content meta-data yield higher accuracy recommendations when compared to the TSVM classifier that utilized only user ratings. (More)

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Paper citation in several formats:
S. Lampropoulos, A.; S. Lampropoulou, P. and A. Tsihrintzis, G. (2011). A MOVIE RECOMMENDER SYSTEM BASED ON ENSEMBLE OF TRANSDUCTIVE SVM CLASSIFIERS.In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 242-247. DOI: 10.5220/0003682802420247

@conference{ncta11,
author={Aristomenis S. Lampropoulos. and Paraskevi S. Lampropoulou. and George A. Tsihrintzis.},
title={A MOVIE RECOMMENDER SYSTEM BASED ON ENSEMBLE OF TRANSDUCTIVE SVM CLASSIFIERS},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)},
year={2011},
pages={242-247},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003682802420247},
isbn={978-989-8425-84-3},
}

TY - CONF

JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)
TI - A MOVIE RECOMMENDER SYSTEM BASED ON ENSEMBLE OF TRANSDUCTIVE SVM CLASSIFIERS
SN - 978-989-8425-84-3
AU - S. Lampropoulos, A.
AU - S. Lampropoulou, P.
AU - A. Tsihrintzis, G.
PY - 2011
SP - 242
EP - 247
DO - 10.5220/0003682802420247

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