A MOVIE RECOMMENDER SYSTEM BASED ON ENSEMBLE OF TRANSDUCTIVE SVM CLASSIFIERS

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

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.

References

  1. Billsus, D. and Pazzani, M. J. (2000). User modeling for adaptive news access. User Modeling and UserAdapted Interaction, 10(2-3):147-180.
  2. Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4):331-370.
  3. Burke, R. (2007). Hybrid web recommender systems. In Brusilovsky, P., Kobsa, A., and Nejdl, W., editors, The adaptive web, pages 377-408. Springer-Verlag, Berlin, Heidelberg.
  4. Chapelle, O., Schölkopf, B., and Zien, A. (2006). SemiSupervised Learning. The MIT Press, Cambridge, Massachusetts, London, England.
  5. Cherkassky, V. and Mulier, F. M. (2007). Learning from Data: Concepts, Theory, and Methods. Wiley-IEEE Press.
  6. Christakou, C. and Stafylopatis, A. (2005). A hybrid movie recommender system based on neural networks. In Proceedings of the 5th International Conference on Intelligent Systems Design and Applications, ISDA 7805, pages 500-505, Washington, DC, USA. IEEE Computer Society.
  7. Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., and Sartin, M. (1999). Combining contentbased and collaborative filters in an online newspaper. In Proc. ACM SIGIR Workshop on Recommender Systems.
  8. Herlocker, J. L., Konstan, J. A., and Riedl, J. (2000). Explaining collaborative filtering recommendations. In Proceedings of the 2000 ACM conference on Computer supported cooperative work, CSCW 7800, pages 241-250, New York, NY, USA. ACM.
  9. Herlocker, J. L., Konstan, J. A., Terveen, L. G., and Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst., 22:5-53.
  10. IMDB (2010). The internet movie database. Database available at http://www.imdb.com/interfaces#plain.
  11. JMDB (2009). Java movie database. Software available at http://www.jmdb.de/.
  12. Joachims, T. (1999). Transductive inference for text classification using support vector machines. In Proceedings of the Sixteenth International Conference on Machine Learning, ICML 7899, pages 200-209, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc.
  13. Joachims, T. (2008). Svmlight: of support vector machines. http://svmlight.joachims.org/.
  14. Kuncheva, L. I. (2004). Combining Pattern Classifiers Methods and Algorithms. Wiley, New York, NY, USA.
  15. Lampropoulos, A. S., Lampropoulou, P. S., and Tsihrintzis, G. A. (2011). A cascade-hybrid music recommender system based on musical genre classification and personality diagnosis for mobile services. Multimedia Tools and Applications, pages 1-18. 10.1007/s11042- 011-0742-0.
  16. Lampropoulos, A. S., Sotiropoulos, D. N., and Tsihrintzis, G. A. (2010). A music recommender based on artificial immune systems. In Howlett, R. J., Jain, L. C., Tsihrintzis, G. A., Damiani, E., Virvou, M., Howlett, R. J., and Jain, L. C., editors, Intelligent Interactive Multimedia Systems and Services, volume 6 of Smart Innovation, Systems and Technologies, pages 167- 179. Springer Berlin Heidelberg.
  17. Mooney, R. J. and Roy, L. (2000). Content-based book recommending using learning for text categorization. In Proceedings of the fifth ACM conference on Digital libraries, DL 7800, pages 195-204, New York, NY, USA. ACM.
  18. MovieLens (2010). Movielens data sets. Dataset available at http://www.grouplens.org/node/73.
  19. Mukherjee, R., Sajja, N., and Sen, S. (2003). A movie recommendation system an application of voting theory in user modeling. User Modeling and User-Adapted Interaction, 13:5-33.
  20. Pazzani, M. J. (1999). A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, 13(5-6):393-408.
  21. Sarwar, B., Karypis, G., Konstan, J., and Reidl, J. (2001). Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web, WWW 7801, pages 285-295, New York, NY, USA. ACM.
  22. Schafer, J. B., Frankowski, D., Herlocker, J., and Sen, S. (2007). Collaborative filtering recommender systems. In Brusilovsky, P., Kobsa, A., and Nejdl, W., editors, The adaptive web, pages 291-324. Springer-Verlag, Berlin, Heidelberg.
  23. Vapnik, V. N. (1982). Estimation of Dependences Based on Empirical Data: Springer Series in Statistics. Springer, Secaucus, NJ, USA.
  24. Vapnik, V. N. (1998). Statistical Learning Theory. Wiley, New York, NY, USA.
  25. Zhu, X. (2008). Semi-supervised learning literature survey. Technical report, University of Winsconsin, Department of Computer Science.
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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