Abstract
Recommender Systems enhance user access to relevant items formation, product by using techniques, such as collaborative and content-based filtering, to select items according to the users personal preferences. Despite the success perspective, the acquisition of these preferences is usually the bottleneck for the practical use of this systems. Active learning approach could be used to minimize the number of requests for user evaluations but the available techniques cannot be applied to collaborative filtering in a straightforward manner. In this paper we propose an original active learning method, named ActiveCP, applied to KNN-based Collaborative Filtering. We explore the concepts of item’s controversy and popularity within a given community of users to select the more informative items to be evaluated by a target user. The experiments testifies that ActiveCP allows the system to learn fast about each user preference, decreasing the required number of evaluations while keeping the precision of the recommendations.
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
Balabanovic, M., Shoham, Y.: Fab: Content-Based,Collaborative Recommendation. Communications of the ACM, 40:3 (1997) 66–72.
Billsus, D., Pazzani, M. J.: Learning Collaborative Information Filters. Fifteenth International Conference on Machine Learning. (1998) 46–54.
Breese, J. S., Heckerman, D., Kadie, C: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence. (1998) 43–52.
Cohen, W. W., Basu, C, Hirsh, H.: Recommendation as Classification: Using Social and Content-Based Information in Recommendation. Proceedings of the AAAI 98. (1998) 714–720.
Cohn, D. A., Atlas, L., Lander, R.: Improving generalization with active learning. Machine Learning, 15:2. (1994). 201–221.
Cotter, P, & Smyth, B.: PTV: Intelligent Personalised TV Guides. Proceedings of the 12th Innovative Applications of Artificial Intelligence Conference.(2000). 957–964.
Hasenjager, M., Ritter, H.: Active Learning with Local Models. Neural Processing Letters, 7:2 (1998) 107–117.
Herlocker, J. L., Konstan, J. A., Borchers, A., Riedl, J.: An Algorithmic framework for performing collaborative filtering. Proceedings of the 1999 Conference on Research and Development in Information Retrieval. (1999).
Lewis, D. D., Catlett, J.: Heterogeneous uncertainty sampling for supervised learning. Machine Learning. (1994). 148–156.
Lindenbaum, M., Markovitch, S., Rusakov, D.: Selective Sampling for Nearest Neighbor Classifiers. AAAI IAAI 99. (1999) 366–371.
Perny, P., Zucker, J. D.: Preference-based Search and Machine Learning for Collaborative Filtering: the “Filme-Conseil” Movie Recommender System. I3 Journal in Information Egineering Sciences, 1:1. (2002).
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: An Open Architecture fo Collaborative Filtering of Netnews. Proceedings of the ACM 1994 Conference on Computer Supported Cooperative Work. (1994) 175–186.
Wilson, D. R., Martinez T. R.: Reduction techniques for exemplar-based learning algorithms. Machine Learning, 38:3 (2000) 257–268.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Teixeira, I.R., de Carvalho, F.d.A.T., Ramalho, G.L., Corruble, V. (2002). ActiveCP: A Method for Speeding up User Preferences Acquisition in Collaborative Filtering Systems. In: Bittencourt, G., Ramalho, G.L. (eds) Advances in Artificial Intelligence. SBIA 2002. Lecture Notes in Computer Science(), vol 2507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36127-8_23
Download citation
DOI: https://doi.org/10.1007/3-540-36127-8_23
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00124-9
Online ISBN: 978-3-540-36127-5
eBook Packages: Springer Book Archive