EasyRec: An easy-to-use, extendable and efficient framework for building industrial recommendation systems
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Abstract (arXiv)
We present EasyRec, an easy-to-use, extendable and efficient recommendation framework for building industrial recommendation systems. Our EasyRec framework is superior in the following aspects: first, EasyRec adopts a modular and pluggable design pattern to reduce the efforts to build custom models; second, EasyRec implements hyper-parameter optimization and feature selection algorithms to improve model performance automatically; third, EasyRec applies online learning to fast adapt to the ever-changing data distribution. The code is released: https://github.com/alibaba/EasyRec.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 6 of its 17 references that are in the map. First posted on arXiv 26 Sep 2022.
Builds on
6 of 17 references.
References · 6
- 2017DeepFM: A Factorization-Machine based Neural Network for CTR Predictioncited by 673
- 2017Deep Interest Network for Click-Through Rate Predictioncited by 541
- 2019Multi-Interest Network with Dynamic Routing for Recommendation at Tmallcited by 113
- 2020Open Benchmarking for Click-Through Rate Predictioncited by 53
- 2021Towards a Better Tradeoff between Effectiveness and Efficiency in Pre-Ranking: A Learnable Feature Selection based Approachcited by 14
- 2017TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworkscited by 4
Built on by
Citing papers per year: 2024: 1; 2025: 2.
3 papers.