There are 37 repositories under recommendation topic.
Best Practices on Recommendation Systems
Easy-to-use,Modular and Extendible package of deep-learning based CTR models .
Officially maintained, supported by PaddlePaddle, including CV, NLP, Speech, Rec, TS, big models and so on.
A Python scikit for building and analyzing recommender systems
Classic papers and resources on recommendation
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.
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
CTR prediction models based on deep learning(基于深度学习的广告推荐CTR预估模型)
An index of recommendation algorithms that are based on Graph Neural Networks. (TORS)
Neural Graph Collaborative Filtering, SIGIR2019
This repository includes some papers that I have read or which I think may be very interesting.
Papers about recommendation systems that I am interested in
RecDB is a recommendation engine built entirely inside PostgreSQL
Paper List for Recommend-system PreTrained Models
MTReclib provides a PyTorch implementation of multi-task recommendation models and common datasets.
Deep-Learning based CTR models implemented by PyTorch
基于tensorflow的个性化电影推荐系统实战(有前端)
A curated list of awesome resources about multimodal recommender systems.
Universal User Representation Pre-training for Cross-domain Recommendation and User Profiling
A PyTorch implementation of Graph Neural Networks for Social Recommendation (GraphRec)
Download and preprocess popular sequential recommendation datasets
Factorization Machines for Recommendation and Ranking Problems with Implicit Feedback Data
A tutorial series by Preferred.AI
This is our implementation of ENMF: Efficient Neural Matrix Factorization (TOIS. 38, 2020). This also provides a fair evaluation of existing state-of-the-art recommendation models.
The official implementation of "Disentangling User Interest and Conformity for Recommendation with Causal Embedding" (WWW '21)