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.

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