Abstract
In this paper we present the Unison-CF algorithm, which provides an efficient way to combine multiple collaborative filtering approaches, drawing advantages from each one of them. Each collaborative filtering approach is treated as a separate component, allowing the Unison-CF algorithm to be easily extended. We evaluate the Unison-CF algorithm by applying it on three existing filtering approaches: User-based Filtering, Item-based Filtering and Hybrid-CF. Adaptation is utilized and evaluated as part of the filtering approaches combination. Our experiments show that the Unison-CF algorithm generates promising results in improving the accuracy and coverage of the existing filtering algorithms.
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
Resnick, P., Iacovou, N., Sushak, M., Bergstrom, P., Riedl, J.: Grouplens: An open architecture for collaborative filtering of netnews. In: ACM 1994 Conference on Computer Supported Cooperative Work, New York, NY, pp. 175–186 (1994)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, WI (1998)
Chen, Y.H., George, E.I.: A bayesian model for collaborative filtering. In: Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics (1999)
Burke, R.: Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction 12, 331–370 (2002)
Balabanovic, M., Shoham, Y.: Fab: Content-based, collaborative recommendation. Communications of the ACMÂ 40 (1997)
Basu, C., Hirsh, H., Cohen, W.: Recommendation as classification: Using social and contentbased information in recommendation. In: Proceedings of the 15th National Conference on Artificial Intelligence, Madison, WI (1998)
Sarwar, B.M., Konstan, J.A., Borchers, A., Herlocker, J., Miller, B., Riedl, J.T.: Using filtering agents to improve prediction quality in the grouplens research collaborative filtering system. In: Conference on Computer Supported Cooperative Work (1998)
Smyth, B., Cotter, P.: Surfing the digital wave: Generation personalized tv listings using collaborative, case-based recommendation. In: Third International Conferece on Case-based Reasoning, Munich, Germany (1999)
Condliff, M.K., Lewis, D.D., Madigan, D., Posse, C.: Bayesian mixed-effects models for recommender systems. In: ACM SIGIR 1999 Workshop on Recommender Systems: Algorithms and Evaluation, Berkeley, CA (1999)
Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering. In: ACM SIGIR Workshop on Recommender Systems, New Orleans, LA (2001)
Vozalis, E., Margaritis, K.G.: On the combination of user-based and item-based collaborative filtering. Technical report, University of Macedonia, Greece (2003)
Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combining content-based and collaborative filters in an online newspaper. In: ACM SIGIR Workshop on Recommender Systems-Implementation and Evaluation, Berkeley, CA (1999)
Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M.: Methods and metrics for cold-start recommendations. In: ACM SIGIR 2002, Tampere, Finland (2002)
Ujjin, S., Bentley, P.J.: Particle swarm optimization recommender system. In: Proceedings of the IEEE Swarm Intelligence Sympoisum 2003, Indianapolis (2003)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems 22, 5–53 (2004)
Shardanand, U., Maes, P.: Social information filtering: Algorithms for automating ’word of mouth’. In: Proceedings of Computer Human Interaction, pp. 210–217 (1995)
Herlocker, J.L.: Understanding and Improving Automated Collaborative Filtering Systems. PhD thesis, University of Minnesota (2000)
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.T.: Item-based collaborative filtering recommendation algorithms. In: 10th International World Wide Web Conference (WWW10), Hong Kong (2001)
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
Vozalis, M., Margaritis, K.G. (2004). Unison-CF: A Multiple-Component, Adaptive Collaborative Filtering System. In: De Bra, P.M.E., Nejdl, W. (eds) Adaptive Hypermedia and Adaptive Web-Based Systems. AH 2004. Lecture Notes in Computer Science, vol 3137. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27780-4_29
Download citation
DOI: https://doi.org/10.1007/978-3-540-27780-4_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22895-0
Online ISBN: 978-3-540-27780-4
eBook Packages: Springer Book Archive