Best Practices on Recommendation Systems

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)

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

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.

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

Machine Learning (EE 5184) in NTU

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

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

An expert system I wrote in high school to recommend fiction to the casual reader. Unfortunately the rule engine it sends commands to is dated and not open source.

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

Code for RecSys'19 paper: Leveraging Post-click Feedback for Content Recommendations

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。

Finding recommendations between them all. Work in progress.

[PY]thon [R]ec[O]mmender [S]ystems library

Recommendation engine in Java. Based on an ALS algorithm (Apache Spark). Train a new model after N seconds.

A real-time news scraping and recommendation system

Implementations of the EigenRec algorithm

A lightweight recommendation algorithm framework based on LycorisNet.

MIT xPRO Data Science Course Movie Recommendations Case Study

Board game recommendation engine

Code the Curve 2020

Movie recommender AI.

Behavior based video recommendation, global keyboard control and ads block for Youtube

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.

A book recommendations application that works on the Dash framework and implements content based filtering using TF-IDF and cosine similarity.

Recommender system

Recommends movies based on user input and a a pre-trained NMF-model with a browser-based user interface (Flask). Optionally outputs movie trailers for recommendations from YouTube.

Projects for Social Gaming course in Technical University of Munich