@inproceedings{6889a96650bb44daac9d7628f409728b,
title = "A high-performance implementation of bayesian matrix factorization with limited communication",
abstract = "Matrix factorization is a very common machine learning technique in recommender systems. Bayesian Matrix Factorization (BMF) algorithms would be attractive because of their ability to quantify uncertainty in their predictions and avoid over-fitting, combined with high prediction accuracy. However, they have not been widely used on large-scale data because of their prohibitive computational cost. In recent work, efforts have been made to reduce the cost, both by improving the scalability of the BMF algorithm as well as its implementation, but so far mainly separately. In this paper we show that the state-of-the-art of both approaches to scalability can be combined. We combine the recent highly-scalable Posterior Propagation algorithm for BMF, which parallelizes computation of blocks of the matrix, with a distributed BMF implementation that users asynchronous communication within each block. 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.",
author = "{Vander Aa}, Tom and Xiangju Qin and Paul Blomstedt and Roel Wuyts and Wilfried Verachtert and Samuel Kaski",
note = "Funding Information: The research leading to these results has received funding from the European Union?s Horizon2020 research and innovation programme under the EPEEC project, grant agreement No 801051. The work was also supported by the Academy of Finland (Flagship programme: Finnish Center for Artificial Intelligence, FCAI; grants 319264, 292334). We acknowledge PRACE for awarding us access to Hazel Hen at GCS@HLRS, Germany. Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 20th International Conference on Computational Science, ICCS 2020 ; Conference date: 03-06-2020 Through 05-06-2020",
year = "2020",
month = jun,
day = "15",
doi = "10.1007/978-3-030-50433-5_1",
language = "English",
isbn = "9783030504328",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer London",
pages = "3--16",
editor = "Krzhizhanovskaya, {Valeria V.} and G{\'a}bor Z{\'a}vodszky and Lees, {Michael H.} and Sloot, {Peter M.A.} and Sloot, {Peter M.A.} and Sloot, {Peter M.A.} and Dongarra, {Jack J.} and S{\'e}rgio Brissos and Jo{\~a}o Teixeira",
booktitle = "Computational Science – ICCS 2020 - 20th International Conference, Proceedings",
address = "United Kingdom",
}