2014 ImprovingPairwiseLearningforIte

• (Rendle & Freudenthaler, 2014) ⇒ Steffen Rendle, and Christoph Freudenthaler. (2014). “Improving Pairwise Learning for Item Recommendation from Implicit Feedback.” In: Proceedings of the 7th ACM International Conference on Web search and data mining. ISBN:978-1-4503-2351-2 doi:10.1145/2556195.2556248

Subject Headings: Bayesian Personalized Ranking.

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Improving+Pairwise+Learning+for+Item+Recommendation+from+Implicit+Feedback
• http://dl.acm.org/citation.cfm?id=2556195.2556248&preflayout=flat#citedby

Quotes

Abstract

Pairwise algorithms are popular for learning recommender systems from implicit feedback. For each user, or more generally context, they try to discriminate between a small set of selected items and the large set of remaining (irrelevant) items. Learning is typically based on stochastic gradient descent (SGD) with uniformly drawn pairs. In this work, we show that convergence of such SGD learning algorithms slows down considerably if the item popularity has a tailed distribution. We propose a non-uniform item sampler to overcome this problem. The proposed sampler is context-dependent and oversamples informative pairs to speed up convergence. An efficient implementation with constant amortized runtime costs is developed. Furthermore, it is shown how the proposed learning algorithm can be applied to a large class of recommender models. The properties of the new learning algorithm are studied empirically on two real-world recommender system problems. The experiments indicate that the proposed adaptive sampler improves the state-of-the art learning algorithm largely in convergence without negative effects on prediction quality or iteration runtime.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 ImprovingPairwiseLearningforIte	Steffen Rendle Christoph Freudenthaler			Improving Pairwise Learning for Item Recommendation from Implicit Feedback				10.1145/2556195.2556248		2014