Abstract
Recommender systems seek to furnish personalized suggestions automatically based on user preferences. These systems use information filtering techniques to recommend new items which has been classified according to one of the three approaches: Content Based Filtering, Collaborative Filtering orhybrid filtering methods. This paper presents a new hybrid filtering approach getting the better qualities of the kNN Collaborative Filtering method with the content filtering one based on Modal Symbolic Data. The main idea is comparing modal symbolic descriptions of users profiles in order to compute the neighborhood of some user in the Collaborative Filtering algorithm. This new approach outperforms, concerning the Find Good Items task measured by half-life utility metric, other three systems: content filtering based on Modal Symbolic Data, kNN Collaborative Filtering based on Pearson Correlation andhybrid Content-Boosted Collaborative approach.
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
Balanovic, M., Shoham, Y.: Fab: Content-based, collaborative recommendation. Communications of the ACM 40, 88–89 (1997)
Bock, H.H., Diday, E.: Analysis of Symbolic Data. Springer, Heidelberg (2000)
Breese, J., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)
Claypool, M., Gokhale, A., Miranda, T., Murnivok, P., Netes, D., Sartin, M.: Combining Content-Based and Collaborative Filters in an Online Newspaper. In: ACM SIGIR Workshop on Recommender Systems, Berkeley, CA, August 19 (1999)
De Carvalho, F.A.T., Bezerra, B.L.D.: Information Filtering based on Modal Symbolic Objects. In: Proceedings of the 26th Annual Conference of the Gesellschaft für Klassifikation (GfKl), pp. 395–404. Springer, Heidelberg (2002)
Herlocker, J., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of SIGIR, pp. 230–237 (1999)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.: Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems 22(1), 5–53 (2004)
Ichino, M., Yaguchi, H.: Generalized Minkowsky Metrics for Mixed Feature Type Data Analysis. IEEE Transactions on System, Man and Cybernetics 24, 698–708 (1994)
Melville, P., Mooney, R.J., Nagarajan, R.: Content-Boosted Collaborative Filtering for Improved Recommendations. In: Proceedings of the Eighteenth National Conference on Artificial Intelligence, pp. 187–192 (2002)
Popescul, A., Ungar, L.H., Pennock, D.M., Lawrence, S.: Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments. In: 17th Conference on Uncertainty in Artificial Intelligence (2001)
Sarwar, B., Konstan, J., Borchers, A., Herlocker, J., Miller, B., Riedl, J.: Using Filtering Agents to Improve Prediction Quality in the Grouplens Research Collaborative Filtering System. In: Proceedings of the ACM Conference on Computer Supported Cooperative Work, pp. 345–354 (1998)
Schafer, J.B., Konstan, J.A., Riedl, J.: E-Commerce Recommendation Applications. Data Mining and Knowledge Discovery 5, 115–153 (2001)
Witten, I.H., Frank, E.: Data Mining - Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Diego, CA (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bezerra, B., Carvalho, F., Alves, G. (2004). Collaborative Filtering Based on Modal Symbolic User Profiles: Knowing You in the First Meeting. In: Lemaître, C., Reyes, C.A., González, J.A. (eds) Advances in Artificial Intelligence – IBERAMIA 2004. IBERAMIA 2004. Lecture Notes in Computer Science(), vol 3315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30498-2_24
Download citation
DOI: https://doi.org/10.1007/978-3-540-30498-2_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23806-5
Online ISBN: 978-3-540-30498-2
eBook Packages: Springer Book Archive