A High-Performance Implementation of Bayesian Matrix Factorization with Limited Communication

no code implementations • 6 Apr 2020 • Tom Vander Aa, Xiangju Qin, Paul Blomstedt, Roel Wuyts, Wilfried Verachtert, Samuel Kaski

We show that the combination of the two methods gives substantial improvements in the scalability of BMF on web-scale datasets, when the goal is to reduce the wall-clock time.

Recommendation Systems Vocal Bursts Intensity Prediction

SMURFF: a High-Performance Framework for Matrix Factorization

no code implementations • 4 Apr 2019 • Tom Vander Aa, Imen Chakroun, Thomas J. Ashby, Jaak Simm, Adam Arany, Yves Moreau, Thanh Le Van, José Felipe Golib Dzib, Jörg Wegner, Vladimir Chupakhin, Hugo Ceulemans, Roel Wuyts, Wilfried Verachtert

Bayesian Matrix Factorization (BMF) is a powerful technique for recommender systems because it produces good results and is relatively robust against overfitting.

Activity Prediction Recommendation Systems +1