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machinelearning_python

机器学习算法python实现

mahout

Mirror of Apache Mahout

matrix-factorization

matrix factorization for recommender system

pace2017

A step-by-step Keras implementation of PACE (Preference And Context Embedding) described in our KDD 2017 paper.

poi-wmf

pretrain-lightfm

Pre-train Embedding in LightFM Recommender System Framework

pumpkin-book

《机器学习》（西瓜书）公式推导解析，在线阅读地址：https://datawhalechina.github.io/pumpkin-book

python3-in-one-pic

Learn python3 in one picture.

python_script-manual

《Python工具代码速查手册》是我们的python培训教材，主要面向数据分析方向。其中包含了python的常用总结性操作，使用jupyter notebook，利用markdown和script结果对常用操作进行总结，包括了使用方式和脚本。之所以使用notebook形式是可以方便大家编辑，方便大家形成自己的总结笔记。当然各位有更好的操作建议也欢迎向我们团队分享~

recbole

A unified, comprehensive and efficient recommendation library

rechorus

“Chorus” of recommendation models: a PyTorch framework for Top-K recommendation with implicit feedback.

recommendsystempractice

《推荐系统实践》代码实现

recsys

The algorithms about Recommender System, such as PMF, PLSA, ALS, RBM and so on.

recwalk

Implementation of the RecWalk recommendation algorithm

rsalgorithms

Some algorithms about traditional and social recommendation.

rspapers

Must-read papers on Recommender System.

sae-nad

The implementation of "Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence"

seven-sorting-methods

实现了常用的排序算法，包括：冒泡排序、直接插入排序、直接选择排序、希尔排序、归并排序、快速排序、堆排序，基于python 3 编程实现

slrc

Short-term and Life-time Repeat Consumption (SLRC) Model for Recommendation

study-floder

相当不错的图书，例如《数学之美》、《浪潮之巅》、《TCP/IP卷一/卷二/卷三》等；一些大的上传受限制的文件《图解TCP_IP_第5版》、《深入理解Java虚拟机JVM》、《effective java》等在README

tmrn

TMRN is the original Theano implementation of the algorithm in "Taxonomy-aware Multi-hop Reasoning Networks for Sequential Recommendation" paper, published at WSDM 2019.

trafficlightsdetection

Traffic Lights Detection and Recoginition with Computer Vision and Embedded Systems

trajectory2vec

code for "Trajectory clustering via deep representation learning"

ts-ppr

Codes for IEEE TKDE 2016 paper: Recommendation for repeat consumption from user implicit feedback

vis-network

:dizzy: Display dynamic, automatically organised, customizable network views.