Abstract
Recommender systems improve access to relevant products and information by making personalized suggestions based on historical data of user’s likes and dislikes. They have become fundamental application in electronic commerce and information access, provide suggestions that effective prune large information spaces so that users are directed toward those item that best meet their needs and preferences. Collaborative filtering and content-based recommending are two fundamental techniques that have been proposed for performing recommendation. Both techniques have their own advantages however they cannot perform well in many situations. To improve performance, various hybrid techniques have been considered. This paper proposes a framework to improve the recommendation performance by combining content-based prediction based on Support Vector Machines and conventional collaborative filtering. The experimental results show that SVMs can improve the performance of the recommender system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using Collaborative Filtering to Weave an Information Tapestry. Communications of the ACM 35(12), 61–70 (1992)
Resnick, P., Iacovou, N., Suchak, M., Bergstorm, P., Riedl, J.: GroupLens: An open architecture for collaborative filtering of netnews. In: Proceedings of 1994 Conference on Computer Supported Collaborative Work, pp. 175–186 (1994)
Shardanand, U., Maes, P.: Social information filtering: Algorithms for automating ”Word of Mouth”. In: Proceeding of ACM CHI 1995, pp. 210–217 (1995)
Hill, W., Stead, L., Rosenstein, M., Riedl, J.: Recommending and Evaluating Choice in a Virtual Community of Use. In: Proceedings of CHI 1995, pp. 194–201 (1995)
Belkin, N.J., Croft, W.B.: Information Filtering and Information Retrieval: Two Sides of the Same Coin?. Communications of ACM 35(12), 29–38 (1992)
Lang, K.: Newsweeder: Learning to Filter News. In: Proceeding of the 12th International Conference on Machine Learning, Lake Tohoe, CA, pp. 331–339 (1995)
Burges, C.: A tutorial on Support Vector Machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)
EachMovie dataset, http://research.compaq.com/SRC/eachmovie
Internet Movie Database, http://www.imdb.com
Pazzani, M., Muramatsu, J., Billsus, D.: Syskill & Webert: Identifying interesting web sites. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence, pp. 54–61 (1996)
Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. In: Proceedings of the Fifth ACM Conference on Digital Libraries, pp. 195–204 (2000)
Mitchell, T.M.: Machine Learning. Mc Graw Hill, New York (1997)
Cristianini, N., Taylor, J.S.: An introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press, Cambridge (2000)
Thubthong, N., Kijsirikul, B.: Support Vector Machines for Thai phoneme recognition. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (2001)
Vapnik, V.: Statistical Learning Theory. Wiley, Chichester (1998)
Friedman, J.H.: Another approach to polychotomous classification. Technical report, Department of Statistics, Standford (1996)
Salton, G.: Automatic text processing: The transformation, analysis, and retrieval of information by Computer. Wesley, London (1989)
Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering. In: Proceeding of the SIGIR-2001 Workshop on Recommender Systems (2001)
Herlocker, J., Konstan, J., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 1999 Conference on Research and Development in Information Retrieval (1999)
Joachims, T.: Transductive inference for text classification using Support Vector Machines. In: Proceeding of 16th International Conference on Machine Learning (1999)
Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J.: GroupLens: Applying collaborative filtering to Usenet news. Communication of the ACM 40(3), 77–87 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rojsattarat, E., Soonthornphisaj, N. (2003). Hybrid Recommendation: Combining Content-Based Prediction and Collaborative Filtering. In: Liu, J., Cheung, Ym., Yin, H. (eds) Intelligent Data Engineering and Automated Learning. IDEAL 2003. Lecture Notes in Computer Science, vol 2690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45080-1_44
Download citation
DOI: https://doi.org/10.1007/978-3-540-45080-1_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40550-4
Online ISBN: 978-3-540-45080-1
eBook Packages: Springer Book Archive