Abstract
The most common classification setting is to predict labels from real-valued vectors, e.g. logistic regression or SupportVector Machines (SVM) are designed for this purpose. Our task differs from this: (1) The variables in our settings are defined over categorical domains with very many levels and there is no a priori knowledge about the space the variable instances lie in. (2) The observed data is highly sparse, nontrivial to interpret and it makes statements rather about pairs of instances than about a single instance. (3) The prediction problem is to rank the instances of one variable given an instance vector (the ‘context’) of the other variables. As the ranking should depend on the given instance vector, it is not a global ranking but a context dependent one. In this chapter, we discuss these three issues and develop a theory for context-aware ranking.
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., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI 1998), pp. 43–52. Morgan Kaufmann, San Francisco (1998)
Buckley, C., Voorhees, E.M.: Evaluating evaluation measure stability. In: SIGIR 2000: Proceedings of the 23rd Annual international ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 33–40. ACM, New York (2000)
Hand, D.J.: Measuring classifier performance: a coherent alternative to the area under the roc curve. Machine Learning 77(1), 103–123 (2009)
Schmidt-Thieme, L.: Compound classification models for recommender systems. In: IEEE International Conference on Data Mining (ICDM 2005), pp. 378–385 (2005)
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Rendle, S. (2010). Ranking from Incomplete Data. In: Context-Aware Ranking with Factorization Models. Studies in Computational Intelligence, vol 330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16898-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-16898-7_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16897-0
Online ISBN: 978-3-642-16898-7
eBook Packages: EngineeringEngineering (R0)