arXiv:2209.12766
공식 코드 · Papers with Code
EasyRec: An easy-to-use, extendable and efficient framework for building industrial recommendation systems
원문 초록
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.
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Personalized Risks and Regulatory Strategies of Large Language Models in Digital Advertising
A Reproducibility Study of Product-side Fairness in Bundle Recommendation
Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection
NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation
Boosting Parameter Efficiency in LLM-Based Recommendation through Sophisticated Pruning
Kamae: Bridging Spark and Keras for Seamless ML Preprocessing
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