cnkuangshi/LightCTR
Lightweight and Scalable framework that combines mainstream algorithms of Click-Through-Rate prediction based computational DAG, philosophy of Parameter Server and Ring-AllReduce collective communication.
LightCTR helps online businesses and e-commerce platforms improve how they predict user engagement. It takes raw user behavior data, click logs, and content information, then processes it to output highly accurate predictions of whether a user will click on an ad or product. This is designed for data scientists, machine learning engineers, and analysts working on large-scale recommendation systems or online advertising.
671 stars. No commits in the last 6 months.
Use this if you need a scalable and lightweight framework to build and train sophisticated click-through rate prediction models on massive, sparse datasets, especially in distributed environments.
Not ideal if you're looking for a general-purpose machine learning library for small datasets or if you don't require high-performance distributed training specific to CTR prediction.
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671
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139
Language
C++
License
Apache-2.0
Category
Last pushed
Jun 17, 2019
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