2013 ActiveLearningandSearchonLowRan

• (Sutherland et al., 2013) ⇒ Dougal J. Sutherland, Barnabás Póczos, and Jeff Schneider. (2013). “Active Learning and Search on Low-rank Matrices.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487627

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Active+Learning+and+Search+on+Low-rank+Matrices
• http://dl.acm.org/citation.cfm?id=2487575.2487627&preflayout=flat#citedby

Quotes

Author Keywords

• Active learning; active search; cold-start; collaborative filtering; data mining; drug discovery; information filtering; matrix factorization; recommender systems

Abstract

Collaborative prediction is a powerful technique, useful in domains from recommender systems to guiding the scientific discovery process. Low-rank matrix factorization is one of the most powerful tools for collaborative prediction. This work presents a general approach for active collaborative prediction with the Probabilistic Matrix Factorization model. Using variational approximations or Markov chain Monte Carlo sampling to estimate the posterior distribution over models, we can choose query points to maximize our understanding of the model, to best predict unknown elements of the data matrix, or to find as many "positive" data points as possible. We evaluate our methods on simulated data, and also show their applicability to movie ratings prediction and the discovery of drug-target interactions.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 ActiveLearningandSearchonLowRan	Jeff Schneider Dougal J. Sutherland Barnabás Póczos			Active Learning and Search on Low-rank Matrices				10.1145/2487575.2487627		2013