Abstract
We introduce a random planted model of bi-categorical data to model the problem of collaborative filtering or categorical clustering. We adapt the ideas of an algorithm due to Condon and Karp [4] to develop a simple linear time algorithm to discover the underlying hidden structure of a graph generated according to the planted model with high probability. We also give applications to the probabilistic analysis of Latent Semantic Indexing (LSI) in the probabilistic corpus models introduced by Papadimitriou et al [12]. We carry out an experimental analysis that shows that the algorithm might work quite well in practice.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Azar, Y., Fiat, A., Karlin, A.R., McSherry, F., Saia, J.: Spectral Analysis of Data. In: STOC 2001(2001)
Balabanovic, M., Shoham, Y.: Fab: Content-Based Collaborative Recommendation. Comm. ACM 3, 66–72 (1997)
Barbour, A.D., Holst, L., Janson, S.: Poisson Approximation. Oxford University Press, Oxford (1992)
Condon, A., Karp, R.: Algorithms for Graph Partitioning on the Planted Partition Model. Random Str. and Algorithms 18, 116–140 (2001)
Feller, W.: An Introduction to Probability Theory and its Applications, 3rd edn., vol. 1. Wiley, Chichester (1968)
Gibson, D., Kleinberg, J., Raghavan, P.: Clustering Categorical Data: An Approach Based on Dynamical Systems. In: Proc. of the 24th VLDB Conference (1998)
Goldberg, D., Nichols, D., Oki, B., Terry, D.: Using collaborative filtering to weave an information tapestry. Comm. ACM 12, 61–70 (1992)
Jerrum, M., Sorkin, G.B.: The Metropolis Algorithm for Graph Bisection. Discrete Appl. Math. 82(1-3), 155–175 (1998)
Henzinger, M.R., Raghavan, P., Rajagopalan, S.: Computing on data streams, SRC Technical Note 1998-2011. Also in DIMACS series in Discrete Mathematics and Theoretical Computer Science 50, 107–118 (1999)
Konstan, J.A., Miller, B.N., Maltz, B., et al.: Grouplens: Applying collaborative filtering to Usenet news. Comm. ACM 40(3), 77–87 (1997)
Maes, P., Sharadanand, M.S.: Social information filtering: algorithms for automating Word of Mouth. In: CHI Proc. 1995 (1995)
Papadimitriou, C., Raghavan, P., Tamaki, H., Vempala, S.: Latent Semantic Indexing: A Probabilistic Analysis. J. Comput. Systems Sciences 61, 217–235 (2000)
Petrov, V.V.: Sums of Independent Random Variables. Springer, Heidelberg (1975)
Sheath, B., Maes, P.: Evolving agents for personalised information filtering. In: Proc. 9th IEEE Conf. on Artificial Intelligence for Applications (1993)
Ungar, L.H., Foster, D.P.: A Formal Statistical Approach to Collaborative Filtering. In: Conference on Automated Learning and Discovery, CONALD (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dubhashi, D., Laura, L., Panconesi, A. (2003). Analysis and Experimental Evaluation of a Simple Algorithm for Collaborative Filtering in Planted Partition Models. In: Pandya, P.K., Radhakrishnan, J. (eds) FST TCS 2003: Foundations of Software Technology and Theoretical Computer Science. FSTTCS 2003. Lecture Notes in Computer Science, vol 2914. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24597-1_15
Download citation
DOI: https://doi.org/10.1007/978-3-540-24597-1_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20680-4
Online ISBN: 978-3-540-24597-1
eBook Packages: Springer Book Archive