Best Practices on Recommendation Systems
-
Updated
Oct 13, 2020 - Python
{{ message }}
Best Practices on Recommendation Systems
Pre-trained and Reproduced Deep Learning Models (『飞桨』官方模型库,包含多种学术前沿和工业场景验证的深度学习模型)
Classic papers and resources on recommendation
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
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
This repository includes some papers that I have read or which I think may be very interesting.
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
OpenRec is an open-source and modular library for neural network-inspired recommendation algorithms
Papers about recommendation systems that I am interested in
RecDB is a recommendation engine built entirely inside PostgreSQL
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
Experimental codes for paper "Outer Product-based Neural Collaborative Filtering".
“Chorus” of recommendation models: a PyTorch framework for Top-K recommendation with implicit feedback.
Deep-Learning based CTR models implemented by PyTorch
Automatic insights discovery and visualization for data analysis.
Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation
This is our implementation of NARRE:Neural Attentional Regression with Review-level Explanations
A PyTorch implementation of Graph Neural Networks for Social Recommendation (GraphRec)
Source code and dataset for KDD 2020 paper "Controllable Multi-Interest Framework for Recommendation"
A recommendation system using tensorflow
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.
基于tensorflow的个性化电影推荐系统实战(有前端)
电影推荐系统、电影推荐引擎、使用Spark完成的电影推荐引擎
Beatmap suggester for osu!
Add a description, image, and links to the recommendation topic page so that developers can more easily learn about it.
To associate your repository with the recommendation topic, visit your repo's landing page and select "manage topics."
For now only strings are accepted as the
measuresparameter inGridSearchCV,RandomizedSearchCV, andcross_validate. It's thus impossible to use those with measures that take specific parameters as input (e.g. #156 ), or to use custom measures.We should then accept callables in addition to strings.
Each callable should only take the
predictionsparameter. In order to handle measur