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JeffChen2020's starred repositories

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.

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agents

TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.

Language:PythonApache-2.02724 78 654

AI_Tutorial

精选机器学习，NLP，图像识别，深度学习等人工智能领域学习资料，搜索，推荐，广告系统架构及算法技术资料整理。算法大牛笔记汇总

2645 83 12

DeepMatch

A deep matching model library for recommendations & advertising. It's easy to train models and to export representation vectors which can be used for ANN search.

Language:PythonApache-2.02142 50 72

RecLearn

Recommender Learning with Tensorflow2.x

Language:PythonMIT1794 35 79

kaggle-CrowdFlower

1st Place Solution for CrowdFlower Product Search Results Relevance Competition on Kaggle.

Language:C++1756 102 4

Awesome-Deep-Learning-Papers-for-Search-Recommendation-Advertising

Awesome Deep Learning papers for industrial Search, Recommendation and Advertising. They focus on Embedding, Matching, Ranking (CTR and CVR prediction), Post Ranking, Multi-task Learning, Graph Neural Networks, Transfer Learning, Reinforcement Learning, Self-supervised Learning and so on.

Language:Python1275 50 1

deep-ctr-prediction

CTR prediction models based on deep learning(基于深度学习的广告推荐CTR预估模型)

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awesome-RecSys-papers

The awesome and classic papers in recommendation system!!! Good luck to every RecSys-learner!

777 69 2

wormhole

Deprecated

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difacto

Distributed Factorization Machines

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SDM

Sequential deep matching model for recommender system at Alibaba

Language:Python199 6 7

Difacto_DMLC

Distributed FM and LR based on Parameter Server with Ftrl

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Yahoo_recommendation

Yahoo! news article recommendation system by linUCB

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AITM

TensorFlow implementation of Adaptive Information Transfer Multi-task (AITM) framework. Code for the paper accepted by KDD21: Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising. [https://arxiv.org/abs/2105.08489]

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Personalized-News-Recommendation

Multi Armed Bandits implementation using the Yahoo! Front Page Today Module User Click Log Dataset

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neural_exploration

Study NeuralUCB and regret analysis for contextual bandit with neural decision

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Multi-armed-Bandits

In this notebook several classes of multi-armed bandits are implemented. This includes epsilon greedy, UCB, Linear UCB (Contextual bandits) and Kernel UCB. Some of the well cited papers in this context are also implemented.

Language:Jupyter NotebookMIT72 2 1