2009 MindtheGapsWeightingtheUnknowni

• (Pan et al., 2009) ⇒ Rong Pan, and Martin Scholz. (2009). “Mind the Gaps: Weighting the Unknown in Large-scale One-class Collaborative Filtering.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557094

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• http://scholar.google.com/scholar?q=%22Mind+the+gaps%3A+weighting+the+unknown+in+large-scale+one-class+collaborative+filtering%22+2009
• http://portal.acm.org/citation.cfm?doid=1557019.1557094&preflayout=flat#citedby

Quotes

Abstract

One-Class Collaborative Filtering (OCCF) is an task that naturally emerges in recommender system settings. Typical characteristics include : Only positive examples can be observed, classes are highly imbalanced, and the vast majority of data points are missing. The idea of introducing weights for missing parts of a matrix has recently been shown to help in OCCF. While existing weighting approaches mitigate the first two problems above, a sparsity preserving solution that would allow to efficiently utilize data sets with e.g., hundred thousands of users and items has not yet been reported. In this paper, we study three different collaborative filtering frameworks : Low-rank matrix approximation, probabilistic latent semantic analysis, and maximum-margin matrix factorization. We propose two novel algorithms for large-scale OCCF that allow to weight the unknowns. Our experimental results demonstrate their effectiveness and efficiency on different problems, including the Netflix Prize data.</sze data.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 MindtheGapsWeightingtheUnknowni	Rong Pan Martin Scholz			Mind the Gaps: Weighting the Unknown in Large-scale One-class Collaborative Filtering		KDD-2009 Proceedings		10.1145/1557019.1557094		2009