Best Practices on Recommendation Systems
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Updated
Nov 5, 2020 - Python
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Best Practices on Recommendation Systems
Learning to Rank in TensorFlow
Multi-Task Deep Neural Networks for Natural Language Understanding
A web app for ranking computer science departments according to their research output in selective venues.
DeText: A Deep Neural Text Understanding Framework for Ranking and Classification Tasks
Case Recommender: A Flexible and Extensible Python Framework for Recommender Systems
Awesome Search - this is all about the (e-commerce) search and its awesomeness
allRank is a framework for training learning-to-rank neural models based on PyTorch.
Fast Differentiable Sorting and Ranking
multi_task_NLP is a utility toolkit enabling NLP developers to easily train and infer a single model for multiple tasks.
Grade exams fast and accurately using a scanner
A collection of BM25 algorithm variants
Comparison tools
A Machine Learning and Optimization framework for Objective-C and Swift (MacOS and iOS)
“Chorus” of recommendation models: a PyTorch framework for Top-K recommendation with implicit feedback.
A PHP Bot that assigns time based server groups on TeamSpeak3.
ElasticCTR,即飞桨弹性计算推荐系统,是基于Kubernetes的企业级推荐系统开源解决方案。该方案融合了百度业务场景下持续打磨的高精度CTR模型、飞桨开源框架的大规模分布式训练能力、工业级稀疏参数弹性调度服务,帮助用户在Kubernetes环境中一键完成推荐系统部署,具备高性能、工业级部署、端到端体验的特点,并且作为开源套件,满足二次深度开发的需求。
Inference algorithms for models based on Luce's choice axiom
Fork and custom implementation of LineUp Library for Visual Analysis of Multi-Attribute
PyTorch and TensorFlow Implementations For A Series Of Deep Learning-Based Recommendation Models (IN PROGRESS)
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