2013 SpeedingUpLargeScaleLearningwit

• (Chakrabarti & Herbrich, 2013) ⇒ Deepayan Chakrabarti, and Ralf Herbrich. (2013). “Speeding Up Large-scale Learning with a Social Prior.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487587

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Speeding+Up+Large-scale+Learning+with+a+Social+Prior
• http://dl.acm.org/citation.cfm?id=2487575.2487587&preflayout=flat#citedby

Quotes

Author Keywords

• Mixture model; parameter learning; social prior

Abstract

Slow convergence and poor initial accuracy are two problems that plague efforts to use very large feature sets in online learning. This is especially true when only a few features are "active" in any training example, and the frequency of activations of different features is skewed. We show how these problems can be mitigated if a graph of relationships between features is known. We study this problem in a fully Bayesian setting, focusing on the problem of using Facebook user-IDs as features, with the social network giving the relationship structure. Our analysis uncovers significant problems with the obvious regularizations, and motivates a two-component mixture-model "social prior” that is provably better. Empirical results on large-scale click prediction problems show that our algorithm can learn as well as the baseline with 12 M fewer training examples, and continuously outperforms it for over 60 M examples. On a second problem using binned features, our model outperforms the baseline even after the latter sees 5x as much data.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 SpeedingUpLargeScaleLearningwit	Ralf Herbrich Deepayan Chakrabarti			Speeding Up Large-scale Learning with a Social Prior				10.1145/2487575.2487587		2013