This project is based on cloud services to create data lake, ETL process, train and deploy learning model to implement a recommendation system.

Bi-TGCF Tensorflow Implementation of BiTGCF： Cross Domain Recommendation via Bi-directional Transfer Graph Collaborative Filtering Networks. in CIKM20

This repository contains code to train and test GNN models for recommendation, mainly using the Deep Graph Library (DGL).

Temporal Meta-path Guided Explainable Recommendation (WSDM2021) TMER Code of paper "Temporal Meta-path Guided Explainable Recommendation". Requirement

SGL This is our Tensorflow implementation for our SIGIR 2021 paper: Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian,and Xing

concierge A continuous learning collaborative filter1 deployed with a light web server2. Distributed updates are live (real time pubsub + delta traini

Group-Buying Recommendation for Social E-Commerce This is the official implementation of the paper Group-Buying Recommendation for Social E-Commerce (

Crab - A Python Library for Recommendation Engines

This repo contains a tensorflow implementation of RecoGCN and the experiment dataset Running the RecoGCN model python train.py Example training outp

PPGN Codes for CIKM 2019 paper Cross-Domain Recommendation via Preference Propagation GraphNet. Citation Please cite our paper if you find this code u

MusicPlayer 基于个性化推荐的音乐播放系统 Hi, 这是我在大四的时候做的毕设，现如今将该项目开源。 本项目是基于Python的tkinter和pygame所著。 该项目总体来说，代码比较烂（因为当时水平很菜）。 运行的话安装几个基本库就能跑，只不过里面的数据还没有上传至Github。 先

Recommendation engines are one of the most well known, widely used and highest value use cases for applying machine learning. Despite this, while there are many resources available for the basics of

🔍 E-Commerce recommender demo 🔍 This is a simple stream setup that uses Memgraph to ingest real-time data from a simulated online store. Data is str

recmetrics A python library of evalulation metrics and diagnostic tools for recommender systems. **This library is activly maintained. My goal is to c

Basic Information: This code is released for the papers: Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang and Meng Wang. A Neural Influence Dif

DMBGN: Deep Multi-Behaviors Graph Networks for Voucher Redemption Rate Prediction The implementation of the accepted paper "Deep Multi-Behaviors Graph

Collaborative Variational Bandwidth Auto-encoder The codes are associated with the following paper: Collaborative Variational Bandwidth Auto-encoder f

recommendersystem Recommendation System to recommend top books from the dataset Introduction The recom.py is the main program code. The dataset is als

A scalable on-line movie recommender using Spark and Flask This Apache Spark tutorial will guide you step-by-step into how to use the MovieLens datase

Content-Based-Movie-Recommender-System This model relies on the similarity of the items being recommended. (I have used Pandas and Numpy. However othe