Best Practices on Recommendation Systems
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Updated
May 17, 2024 - Python
Best Practices on Recommendation Systems
Officially maintained, supported by PaddlePaddle, including CV, NLP, Speech, Rec, TS, big models and so on.
Easy-to-use,Modular and Extendible package of deep-learning based CTR models .
A Python scikit for building and analyzing recommender systems
Classic papers and resources on recommendation
Fast Python Collaborative Filtering for Implicit Feedback Datasets
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.
基于金融-司法领域(兼有闲聊性质)的聊天机器人,其中的主要模块有信息抽取、NLU、NLG、知识图谱等,并且利用Django整合了前端展示,目前已经封装了nlp和kg的restful接口
An Open-source Toolkit for Deep Learning based Recommendation with Tensorflow.
CTR prediction models based on deep learning(基于深度学习的广告推荐CTR预估模型)
Neural Graph Collaborative Filtering, SIGIR2019
推荐、广告工业界经典以及最前沿的论文、资料集合/ Must-read Papers on Recommendation System and CTR Prediction
This repository includes some papers that I have read or which I think may be very interesting.
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
An index of recommendation algorithms that are based on Graph Neural Networks. (TORS)
CRSLab is an open-source toolkit for building Conversational Recommender System (CRS).
“Chorus” of recommendation models: a light and flexible PyTorch framework for Top-K recommendation.
OpenRec is an open-source and modular library for neural network-inspired recommendation algorithms
Papers about recommendation systems that I am interested in
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