Scalable Realistic Recommendation Datasets through Fractal Expansions

Scalable Realistic Recommendation Datasets through Fractal Expansions

23 January 2019

Francois Belletti

Yi-Fan Chen

John R. Anderson

Papers citing "Scalable Realistic Recommendation Datasets through Fractal Expansions"

5 / 5 papers shown

Title
Flexible Generation of Preference Data for Recommendation Analysis Simone Mungari Erica Coppolillo Ettore Ritacco Giuseppe Manco 35 1 0 23 Jul 2024
Analysis and Optimization of GNN-Based Recommender Systems on Persistent Memory Yuwei Hu Jiajie Li Zhongming Yu Zhiru Zhang GNN 39 0 0 25 Jul 2022
SmartSAGE: Training Large-scale Graph Neural Networks using In-Storage Processing Architectures Yunjae Lee Jin-Won Chung Minsoo Rhu GNN 37 49 0 10 May 2022
MLPerf Training Benchmark Arya D. McCarthy Christine Cheng Cody Coleman Greg Diamos Paulius Micikevicius ... Carole-Jean Wu Lingjie Xu Masafumi Yamazaki C. Young Matei A. Zaharia 47 307 0 02 Oct 2019
How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility A. Chaney Brandon M Stewart Barbara E. Engelhardt CML 169 314 0 30 Oct 2017