Abstract
Recommender systems are used by an increasing number of e-commerce websites to help the customers to find suitable products from a large database. One of the most popular techniques for recommender systems is collaborative filtering. Several collaborative filtering algorithms claim to be able to solve i) the new-item problem, when a new item is introduced to the system and only a few or no ratings have been provided; and ii) the user-bias problem, when it is not possible to distinguish two items, which possess the same historical ratings from users, but different contents. However, for most algorithms, evaluations are not satisfying due to the lack of suitable evaluation metrics and protocols, thus, a fair comparison of the algorithms is not possible.
In this paper, we introduce new methods and metrics for evaluating the user-bias and new-item problem for collaborative filtering algorithms which consider attributes. In addition, we conduct empirical analysis and compare the results of existing collaborative filtering algorithms for these two problems by using several public movie datasets on a common setting.
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
BREESE, J.S., HECKERMAN, D., and KADIE, C. (1998): Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth Annual Confer-ence on Uncertainty in Artificial Intelligence, pp. 43-52, July 1998.
BURKE, R. (2002): Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction. vol. 12(4), pp. 331-370.
HERLOCKER, J.L., KONSTAN, J.A., TERVEEN, L.G. and RIEDL, J.T. (2004): Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, vol. 22, no. 1, pp. 5-53, 2004.
HOFMANN, T. (2004): Latent Semantic Models for Collaborative Filtering. ACM Transac-tions on Information Systems, 2004, Vol 22(1), pp. 89-115.
KIM, B.M. and LI, Q. (2004): Probabilistic Model Estimation for Collaborative Filtering Based on Item Attributes. IEEE International Conference on Web Intelligence.
MELVILLE, P., MOONEY, R. and NAGARAJAN, R. (2002): Contentboosted collaborative filtering. In Proceedings of Eighteenth National Conference on Artificial Intelligence (AAAI-2002), pp. 187-192.
SARWAR, B.M., KARYPIS, G., KONSTAN, J.A. and RIEDL, J. (2000): Analysis of recom-mendation algorithms for e-commerce. In Proceedings of the Second ACM Conference on Electronic Commerce (ECÕ00), 2000, pp. 285-295.
SARWAR, B.M., KARYPIS, G., KONSTAN, J.A. and RIEDL, J. (2001): Itembased collab-orative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web. New York, NY, USA: ACM Press, 2001, pp. 285-295.
SCHEIN, A.I., POPESCUL, A., UNGAR, L.H. and PENNOCK, D.M. (2002):Methods and metrics for cold-start recommendations. In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval. New York, NY, USA: ACM Press, 2002, pp. 253-260.
TSO, K. and SCHMIDT-THIEME L. (2005): Attribute-aware Collaborative Filtering. In Pro-ceedings of 29th Annual Conference of the Gesellschaft für Klassifikation (GfKl) 2005, Magdeburg, Springer.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hauger, S., Tso, K.H.L., Schmidt-Thieme, L. (2008). Comparison of Recommender System Algorithms Focusing on the New-item and User-bias Problem. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L., Decker, R. (eds) Data Analysis, Machine Learning and Applications. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78246-9_62
Download citation
DOI: https://doi.org/10.1007/978-3-540-78246-9_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78239-1
Online ISBN: 978-3-540-78246-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)