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:
Notes
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 | |
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2015 DynamicMatrixFactorizationwithP | Amin Mantrach Robin Devooght Nicolas Kourtellis | Dynamic Matrix Factorization with Priors on Unknown Values | 10.1145/2783258.2783346 | 2015 |