Abstract
We propose a modified version of our collaborative filtering method using restoration operators, which was proposed in [6]. Our previous method was designed so as to minimize expected squared error of predictions for user’s ratings, and we experimentally showed that, for users who have evaluated only small number of items, mean squared error of our method is smaller than that of correlation-base methods. After further experiments, however, we found that, for users who have evaluated many items, the best correlation-based method has smaller mean squared error than our method. In our modified version, we incorporated an idea of projecting on a low-dimensional subspace with our method using restoration operators. We experimentally showed that our modification overcame the shortcoming stated above.
This work was partly supported by Grant-in-Aid for Scientific Research (B), No. 14380151, from Japan Society for the Promotion of Science.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Billsus, D., Pazzani, M.: Learning Collaborative Information Filters. In: Proc. of the 15th International Conference (ICML 1998), pp. 46–54 (1998)
Breese, J., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proc. of the 14th Annu. Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)
EachMovie collaborative filtering data set (1997), research.compaq.com/SRC/eachmovie/
Lee, W.: Collaborative Learning for Recommender Systems. In: Proc. of the 18th International Conf. on Machine Learning, pp. 314–321 (2001)
Nakamura, A., Abe, N.: Collaborative Filtering using Weighted Majority Prediction Algorithms. In: Proc. of 15th International Conference on Machine Learning, pp. 395–403 (1998)
Nakamura, A., Kudo, M., Tanaka, A.: Collaborative Filtering using Restoration Operators. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 339–349. Springer, Heidelberg (2003)
Ogawa, H., Oja, E.: Projection Filter, Wiener Filter, and Karhunen-Loève Subspaces in Digital Image Restoration. Journal of Mathematical analysis and applications 114, 37–51 (1986)
Resnick, P., Iacovou, N., Suchak, M., Bergstom, P., Riedl, J.: GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In: Proc. of CSCW, pp. 175–186 (1994)
Shardanand, U., Maes, P.: Social Information Filtering: Algorithms and Automating “Word of Mouth”. In: Proc. of CHI 1995, pp. 210–217 (1995)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Application of Dimensionality Reduction in Recommender System – a Case Study. In: Web KDD 2000 Web Mining for E-commerce Workshop (2000)
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
Nakamura, A., Kudo, M., Tanaka, A., Tanabe, K. (2003). Collaborative Filtering Using Projective Restoration Operators. In: Grieser, G., Tanaka, Y., Yamamoto, A. (eds) Discovery Science. DS 2003. Lecture Notes in Computer Science(), vol 2843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39644-4_38
Download citation
DOI: https://doi.org/10.1007/978-3-540-39644-4_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20293-6
Online ISBN: 978-3-540-39644-4
eBook Packages: Springer Book Archive