2013 ScalableSupervisedDimensionalit

• (Raeder et al., 2013) ⇒ Troy Raeder, Claudia Perlich, Brian Dalessandro, Ori Stitelman, and Foster Provost. (2013). “Scalable Supervised Dimensionality Reduction Using Clustering.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2488208

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Scalable+Supervised+Dimensionality+Reduction+Using+Clustering
• http://dl.acm.org/citation.cfm?id=2487575.2488208&preflayout=flat#citedby

Quotes

Author Keywords

• Applications; clustering; supervised dimensionality reduction

Abstract

The automated targeting of online display ads at scale requires the simultaneous evaluation of a single prospect against many independent models. When deciding which ad to show to a user, one must calculate likelihood-to-convert scores for that user across all potential advertisers in the system. For modern machine-learning-based targeting, as conducted by Media6Degrees (M6D), this can mean scoring against thousands of models in a large, sparse feature space. Dimensionality reduction within this space is useful, as it decreases scoring time and model storage requirements. To meet this need, we develop a novel algorithm for scalable supervised dimensionality reduction across hundreds of simultaneous classification tasks. The algorithm performs hierarchical clustering in the space of model parameters from historical models in order to collapse related features into a single dimension. This allows us to implicitly incorporate feature and label data across all tasks without operating directly in a massive space. We present experimental results showing that for this task our algorithm outperforms other popular dimensionality-reduction algorithms across a wide variety of ad campaigns, as well as production results that showcase its performance in practice.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 ScalableSupervisedDimensionalit	Foster Provost Brian Dalessandro Claudia Perlich Ori Stitelman Troy Raeder			Scalable Supervised Dimensionality Reduction Using Clustering				10.1145/2487575.2488208		2013