There are 39 repositories under ctr-prediction topic.
Tensorflow implementation of DeepFM for CTR prediction.
Factorization Machine models in PyTorch
CTR prediction models based on deep learning(基于深度学习的广告推荐CTR预估模型)
DeepTables: Deep-learning Toolkit for Tabular data
Recommender Systems Paperlist that I am interested in
Tensorflow implementation of Product-based Neural Networks. An extended version is at https://github.com/Atomu2014/product-nets-distributed.
A PyTorch implementation of DeepFM for CTR prediction problem.
A Lighting Pytorch Framework for Recommendation Models, Easy-to-use and Easy-to-extend.
Paper List for Recommend-system PreTrained Models
Wide and Deep Learning for CTR Prediction in tensorflow
4th Place Solution for Mercari Price Suggestion Competition on Kaggle using DeepFM variant.
A collection of research and application papers of (uncertainty) calibration techniques.
MTReclib provides a PyTorch implementation of multi-task recommendation models and common datasets.
Deep-Learning based CTR models implemented by PyTorch
rec_pangu is a flexible open-source project for recommendation systems. It incorporates diverse AI models like ranking algorithms, sequence recall, multi-interest models, and graph-based techniques. Designed for both beginners and advanced users, it enables rapid construction of efficient, custom recommendation engines.
Lifelong sequential modeling for user response prediction. A comprehensive evaluation framework for our SIGIR 2019 paper.
Hybrid model of Gradient Boosting Trees and Logistic Regression (GBDT+LR) on Spark
Wide and Deep Learning(Wide&ResDNN) for Kaggle Criteo Dataset in tensorflow
[CIKM 2019] Code and dataset for "Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction"
The collection of papers about recommender system
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机器学习、深度学习基础知识. 推荐系统及nlp相关算法实现
Code for paper "Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning"