Best Practices on Recommendation Systems
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Updated
Jul 1, 2021 - Python
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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)
Easy-to-use,Modular and Extendible package of deep-learning based CTR models .
Classic papers and resources on recommendation
An Open-source Toolkit for Deep Learning based Recommendation with Tensorflow.
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
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)
This repository includes some papers that I have read or which I think may be very interesting.
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
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.
自动化数据探索分析和智能可视化设计应用. Automatic insights discovery and visualization for data analysis.
Deep-Learning based CTR models implemented by PyTorch
Source code and dataset for KDD 2020 paper "Controllable Multi-Interest Framework for Recommendation"
Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation
Experimental codes for paper "Outer Product-based Neural Collaborative Filtering".
A PyTorch implementation of Graph Neural Networks for Social Recommendation (GraphRec)
基于tensorflow的个性化电影推荐系统实战(有前端)
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.
Graph Neural Network based Social Recommendation Model. SIGIR2019.
This is our implementation of NARRE:Neural Attentional Regression with Review-level Explanations
Disentagnled Graph Collaborative Filtering, SIGIR2020
A tutorial series by Preferred.AI
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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