Abstract
In this work we perform an analysis of probabilistic approaches to recommendation upon a different validation perspective, which focuses on accuracy metrics such as recall and precision of the recommendation list. Traditionally, state-of-art approches to recommendations consider the recommendation process from a “missing value prediction” perspective. This approach simplifies the model validation phase that is based on the minimization of standard error metrics such as RMSE. However, recent studies have pointed several limitations of this approach, showing that a lower RMSE does not necessarily imply improvements in terms of specific recommendations. We demonstrate that the underlying probabilistic framework offers several advantages over traditional methods, in terms of flexibility in the generation of the recommendation list and consequently in the accuracy of recommendation.
Chapter PDF
Similar content being viewed by others
References
Agarwal, D., Chen, B.-C.: flda: matrix factorization through latent dirichlet allocation. In: WSDM, pp. 91–100 (2010)
Barbieri, N., Guarascio, M., Manco, G.: A probabilistic hierarchical approach for pattern discovery in collaborative filtering data. In: SMD (2011)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)
Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: ACM RecSys, pp. 39–46 (2010)
Cremonesi, P., Turrin, R., Lentini, E., Matteucci, M.: An evaluation methodology for collaborative recommender systems. In: AXMEDIS, pp. 224–231 (2008)
Ge, M., Delgado-Battenfeld, C., Jannach, D.: Beyond accuracy: evaluating recommender systems by coverage and serendipity. In: ACM RecSys, pp. 257–260 (2010)
Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Communications of the ACM 35(12), 61–70 (1992)
Hofmann, T.: Collaborative filtering via gaussian probabilistic latent semantic analysis. In: SIGIR (2003)
Hofmann, T.: Latent semantic models for collaborative filtering. ACM Transactions on Information Systems (TOIS) 22(1), 89–115 (2004)
Hofmann, T., Puzicha, J.: Latent class models for collaborative filtering. IJCAI, 688–693 (1999)
Jin, X., Zhou, Y., Mobasher, B.: A maximum entropy web recommendation system: combining collaborative and content features. In: KDD, pp. 612–617 (2005)
Koren, Y.: How useful is a lower rmse? (2007), http://www.netflixprize.com/community/viewtopic.php?id=828
Marlin, B.: Modeling user rating profiles for collaborative filtering. In: NIPS (2003)
Marlin, B., Marlin, B.: Collaborative filtering: A machine learning perspective. Tech. rep., Department of Computer Science University of Toronto (2004)
McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough: How accuracy metrics have hurt recommender systems. In: ACM SIGCHI Conference on Human Factors in Computing Systems, pp. 1097–1101 (2006)
Pazzani, M.J., Billsus, D.: Content-based recommendation systems. The Adaptive Web: Methods and Strategies of Web Personalization, 325–341 (2007)
Salakhutdinov, R., Mnih, A.: Bayesian probabilistic matrix factorization using markov chain monte carlo. In: ICML, pp. 880–887 (2008)
Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: NIPS, pp. 1257–1264 (2008)
Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW, pp. 285–295 (2001)
Shan, H., Banerjee, A.: Generalized probabilistic matrix factorizations for collaborative filtering. In: ICDM (2010)
Stern, D.H., Herbrich, R., Graepel, T.: Matchbox: large scale online bayesian recommendations. In: WWW, pp. 111–120 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Barbieri, N., Manco, G. (2011). An Analysis of Probabilistic Methods for Top-N Recommendation in Collaborative Filtering. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2011. Lecture Notes in Computer Science(), vol 6911. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23780-5_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-23780-5_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23779-9
Online ISBN: 978-3-642-23780-5
eBook Packages: Computer ScienceComputer Science (R0)