Best Practices on Recommendation Systems
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Updated
May 16, 2024 - Python
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
Fast Python Collaborative Filtering for Implicit Feedback Datasets
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.
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
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
“Chorus” of recommendation models: a light and flexible PyTorch framework for Top-K recommendation.
CRSLab is an open-source toolkit for building Conversational Recommender System (CRS).
OpenRec is an open-source and modular library for neural network-inspired recommendation algorithms
Source code and dataset for KDD 2020 paper "Controllable Multi-Interest Framework for Recommendation"
MTReclib provides a PyTorch implementation of multi-task recommendation models and common datasets.
基于tensorflow的个性化电影推荐系统实战(有前端)
Deep-Learning based CTR models implemented by PyTorch
[WWW'2024] "RLMRec: Representation Learning with Large Language Models for Recommendation"
Universal User Representation Pre-training for Cross-domain Recommendation and User Profiling
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