recommendation-algorithm

Best Practices on Recommendation Systems

A TensorFlow recommendation algorithm and framework in Python.

CTR prediction models based on deep learning(基于深度学习的广告推荐CTR预估模型)

A collection of resources for Recommender Systems (RecSys)

A collection of resources for Recommender Systems (RecSys)

OpenRec is an open-source and modular library for neural network-inspired recommendation algorithms

⚡ A python fast implementation of the famous SVD algorithm popularized by Simon Funk during Netflix Prize

pyRecLab is a library for quickly testing and prototyping of traditional recommender system methods, such as User KNN, Item KNN and FunkSVD Collaborative Filtering. It is developed and maintained by Gabriel Sepúlveda and Vicente Domínguez, advised by Prof. Denis Parra, all of them in Computer Science Department at PUC Chile, IA Lab and SocVis Lab.

Collaborative filtering recommendation system. Recommendation algorithm using collaborative filtering. Topics: Ranking algorithm, euclidean distance algorithm, slope one algorithm, filtragem colaborativa.

A Lighting Pytorch Framework for Recommendation System, Easy-to-use and Easy-to-extend.

一个基于SSM框架与物品的协同过滤算法(ItemCF)的简单电子书推荐系统

Machine Learning (EE 5184) in NTU

PHP and wrapping Redis's sorted set APIs for specializing recommending operations.

基于协同过滤算法的个性化新闻推荐系统的设计与实现（采用Java语言的SpringBoot和SSM两种框架分别实现基于用户、物品的协同过滤推荐算法）

商品关联关系挖掘，使用Spring Boot开发框架和Spark MLlib机器学习框架，通过FP-Growth算法，分析用户的购物车商品数据，挖掘商品之间的关联关系。项目对外提供RESTFul接口。

This is a new deep learning model for recommender system, which we called PHD

🎥 Movie Recommender AI System

🔱 Some recognized algorithms[Decision Tree, Adaboost, Perceptron, Clustering, Neural network etc. ] of machine learning and pattern recognition are implemented from scratch using python. Data sets are also included to test the algorithms.

Finding recommendations between them all. Work in progress.

基于Springboot的智慧教学平台的系统设计与实现（智慧教学推荐系统），可以进行学习分析和个性化推荐，系统可以根据学生的学习行为和表现进行分析，生成学习分析报告。根据学生的学习情况和需求，系统可以提供个性化的学习推荐和建议，推荐适合的课程和教学资源推荐（根据用户点击量或浏览量推荐）。