2015 DynamicMatrixFactorizationwithP

• (Devooght et al., 2015) ⇒ Robin Devooght, Nicolas Kourtellis, and Amin Mantrach. (2015). “Dynamic Matrix Factorization with Priors on Unknown Values.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783346

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222015%22+Dynamic+Matrix+Factorization+with+Priors+on+Unknown+Values
• http://dl.acm.org/citation.cfm?id=2783258.2783346&preflayout=flat#citedby

Quotes

Author Keywords

• Collaborative filtering; information filtering; matrix factorization; recommender systems; sparse, structured, and very large systems

Abstract

Advanced and effective collaborative filtering methods based on explicit feedback assume that unknown ratings do not follow the same model as the observed ones (not missing at random). In this work, we build on this assumption, and introduce a novel dynamic matrix factorization framework that allows to set an explicit prior on unknown values. When new ratings, users, or items enter the system, we can update the factorization in time independent of the size of data (number of users, items and ratings). Hence, we can quickly recommend items even to very recent users. We test our methods on three large datasets, including two very sparse ones, in static and dynamic conditions. In each case, we outrank state-of-the-art matrix factorization methods that do not use a prior on unknown ratings.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 DynamicMatrixFactorizationwithP	Amin Mantrach Robin Devooght Nicolas Kourtellis			Dynamic Matrix Factorization with Priors on Unknown Values				10.1145/2783258.2783346		2015