The Potential of AutoML for Recommender Systems

6 February 2024

Papers citing "The Potential of AutoML for Recommender Systems"

10 / 10 papers shown

Title
MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis Jiancheng Yang Rui Shi Bingbing Ni VLM 90 304 0 28 Oct 2020
Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization Rohan Anand Joeran Beel 21 19 0 19 Aug 2020
AutoRec: An Automated Recommender System Ting-Hsiang Wang Qingquan Song Xiaotian Han Zirui Liu Haifeng Jin Xia Hu 61 21 0 26 Jun 2020
Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL Lucas Zimmer Marius Lindauer Frank Hutter MU 118 92 0 24 Jun 2020
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data Nick Erickson Jonas W. Mueller Alexander Shirkov Hang Zhang Pedro Larroy Mu Li Alex Smola LMTD 218 628 0 13 Mar 2020
FLAML: A Fast and Lightweight AutoML Library Chi Wang Qingyun Wu Markus Weimer Erkang Zhu 76 203 0 12 Nov 2019
Generating Personalized Recipes from Historical User Preferences Bodhisattwa Prasad Majumder Shuyang Li Jianmo Ni Julian McAuley 60 114 0 31 Aug 2019
Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches Maurizio Ferrari Dacrema Paolo Cremonesi Dietmar Jannach 50 586 0 16 Jul 2019
Auto-Keras: An Efficient Neural Architecture Search System Haifeng Jin Qingquan Song Xia Hu 107 808 0 27 Jun 2018
AMC: AutoML for Model Compression and Acceleration on Mobile Devices Yihui He Ji Lin Zhijian Liu Hanrui Wang Li Li Song Han 95 1,347 0 10 Feb 2018