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autodebias

This is the official pytorch implementation of AutoDebias, an automatic debiasing method for recommendation.

awesome-rspapers

Recommender System Papers

cnn-ucb

codband

Code for When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution, SIGIR 2021

debiasing-item2item

Implementation of "Debiasing Item-to-Item Recommendations With Small Annotated Datasets" (RecSys '20)

deepctr-torch

【PyTorch】Easy-to-use,Modular and Extendible package of deep-learning based CTR models.

fasttext

Library for fast text representation and classification.

gmcf_neural_graph_matching_based_collaborative_filtering

GMCF: Neural Graph Matching based Collaborative Filtering, SIGIR 2021

gml-fm

Official implementation for our TKDE paper.

java-airplane-game

入门级飞机小游戏

jvm-demo

手写JVM—Java实现

kcode

2020 KuaiShou Kcode

kdd2021_mufasa

linux-nova

NOVA is a log-structured file system designed for byte-addressable non-volatile memories, developed at the University of California, San Diego.

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。

movie_recommender

MovieLens based recommender system.使用MovieLens数据集训练的电影推荐系统。

ncf

A pytorch implementation of He et al. "Neural Collaborative Filtering" at WWW'17

neural-collaborative-filtering

pytorch version of neural collaborative filtering

neural_exploration

Study NeuralUCB and regret analysis for contextual bandit with neural decision

nova

NOVA: NOn-Volatile memory Accelerated log-structured file system

pytorch_ngcf

PyTorch Implemenation for Neural Graph Collaborative Filtering

recommendersystems

推荐系统

recommondation-system

一些传统推荐算法的实现，包括基于内容的推荐，协同过滤，矩阵分解

sigir20_kdcrec

Experiments codes for SIGIR '20 paper "A General Knowledge Distillation Framework for Counterfactual Recommendation via Uniform Data"

wsdm21-mainstream

Code and data for WSDM'21 paper "Leave No User Behind: Towards Improving the Utility of Recommender Systems for Non-mainstream Users"