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acf

Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention

ai-recommendersystem

该仓库尝试整理推荐系统领域的一些经典算法模型

alitanet

AlitaNet: A click through rate (ctr) prediction deep learning Network implementation with TensorFlow, including LR, FM, AFM, Wide&Deep, DeepFM, xDeepFM, AutoInt, FiBiNet, LS-PLM, DCN, etc.

bayesianrs

Recommendation System with Bayesian inference

bdci-2020-timeseries-prediction

Matrix Factorization for High-Dimensional and Sparse Time Series Prediction

cf_gbdt_lr

简单的实现推荐系统的召回模型和排序模型，其中召回模型使用协同过滤算法，排序模型使用gbdt+lr算法

cmf-ctf

Outlier-Resilient Web Service QoS Prediction

collaborativefilteringv3

TimeSVD++ model: Recommender system using Matrix Factorization techniques, while utilize temporal models to extend models accuracy.

context-aware-sequential-recommendation

This is the Github repository containing the code for the Context-Aware Sequential Recommendation project for the Information Retrieval 2 course at the University of Amsterdam

deep-recommender-system

深度学习在推荐系统中的应用及论文小结。

deeplearningfortsf

深度学习以进行时间序列预测

deeptransrec

TransRec in python3, implemented with tensorflow1.2.

fm-by-pytorch

利用pytorch框架实现因子分解机FM算法

fm_tensorflow

Factorization Machines implementation with Tensorflow

fpmc

Python implementation of "Factorizing Personalized Markov Chains for Next-Basket Recommendation"

gbdt_lr

广告点击率(CTR)预测经典模型 GBDT + LR 理解与实践（附数据 + 代码）

gml-fm

Official implementation for our TKDE paper.

gongyu-recommend

hgn

Hierarchical Gating Networks for Sequential Recommendation

k_means_clustering_movie_recommendations

Movie ratings datasets from MovieLens

kaggle_criteo_ctr_challenge-

This is a kaggle challenge project called Display Advertising Challenge by CriteoLabs at 2014.这是2014年由CriteoLabs在kaggle上发起的广告点击率预估挑战项目。

librec

LibRec: A Leading Java Library for Recommender Systems, see

mall

mall项目是一套电商系统，包括前台商城系统及后台管理系统，基于SpringBoot+MyBatis实现，采用Docker容器化部署。 前台商城系统包含首页门户、商品推荐、商品搜索、商品展示、购物车、订单流程、会员中心、客户服务、帮助中心等模块。 后台管理系统包含商品管理、订单管理、会员管理、促销管理、运营管理、内容管理、统计报表、财务管理、权限管理、设置等模块。

meantime

movie

Personalized real-time movie recommendation system

movie_recommend

基于协同过滤和spark-als的电影推荐系统

movielens

movierecommend

基于Spring Boot的大数据电影推荐系统，采用协同过滤算法实现个性化推荐，前端地址https://github.com/erxuesun/moviefront

movierecommendation

UserCf和ItemCF协同过滤推荐算法的实现

movierecommendation-1

本项目使用两种算法来实现一个电影推荐系统，一个是CNN，另一个是矩阵分解的协同过滤。