DiFacto: Distributed Factorization Machines

By Mu Li, Ziqi Liu, Alex Smola, Yu-Xiang Wang. 2016

56 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Kun Gai
2. Xiaoqiang Zhu
3. Shengping Zhou
4. Di Niu
5. Jianming Yang
6. Han Zhu
7. Xiao Ma
8. Han Li

In Information Systems

1. Xiaoqiang Zhu
2. Kun Gai
3. Han Zhu
4. Han Li
5. Guorui Zhou

In Management Science and Operations Research

1. Xiaoqiang Zhu
2. Kun Gai
3. Guorui Zhou

Citations in the corpus

• Deep Interest Network for Click-Through Rate Prediction (2017, background): “feature ids in each sample. •Filter. Filter visited дoods_id by occurrence frequency in samplesandleaveonlythemostfrequentones.Inoursetting, top 20 million дoods_ids are left. •RegularizationinDiFacto[16].Parametersassociatedwith frequent features are less over-regularized. •MBA.OurproposedMini-Batc”
• Proposal Scaling Distributed Machine Learning with System and Algorithm Co-design (2016, unknown): “The results were published in WSDM’16 [12].”
• FDML: A Collaborative Machine Learning Framework for Distributed Features (2019, background): “A similar bounded delay condition is enforced in most Parameter-Server-like systems [1, 5, 14, 17, 19] to ensure convergence and avoid chaotic behavior of a completely asynchronous system.”