A MOVIE RECOMMENDER SYSTEM BASED ON ENSEMBLE OF TRANSDUCTIVE SVM CLASSIFIERS

Aristomenis S. Lampropoulos, Paraskevi S. Lampropoulou, George A. Tsihrintzis

2011

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.

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Paper Citation


in Harvard Style

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


in Bibtex Style

@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},
}


in EndNote Style

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