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acam-model

It is deep recommendation model with attribute-level co-attention

attention-recommendation

基于Keras框架的推荐系统的多个经典模型实现及添加自注意力机制的实现

bloodhound

Six Degrees of Domain Admin

collaborativememorynetwork

Collaborative Memory Network for Recommendation Systems, SIGIR 2018

e-commerce-recommendations

Recommendation System for E-Commerce Project (Python)

eckg

电商行业知识图谱：构建电商行业实体关系，应用于商品推荐，商品搭配，问答系统。

film-recommendation-system

基于知识图谱的电影推荐系统

gcn-lpa

A tensorflow implementation of GCN-LPA

hkipn

Implementation of paper: A Hierarchical Knowledge and Interest Propagation Network for Recommender Systems

kac-model

It includes the source codes of a deep knowledge-based recommendation KAC, and the datasets of Douban movie and NetEase music to evaluate the model

kdd2018_mpcn

Code for our KDD 2018 Paper "Multi-Pointer Co-Attention Networks for Recommendation"

kgcn

A tensorflow implementation of Knowledge Graph Convolutional Networks

knowledge_graph_attention_network

KGAT: Knowledge Graph Attention Network for Recommendation, KDD2019

knowledgegraph

demos of KG

knowledgegraphdata

史上最大规模1.4亿中文知识图谱开源下载

lightgcn

mcrec

Source code for KDD 2018 paper "Leverage Meta-path based Context for Top-N Recommendation with a Neural Co-Attention Model"

mkr

A tensorflow implementation of MKR (Multi-task Learning for Knowledge Graph Enhanced Recommendation)

movie-recommendation-system

An ML assignment to demonstrate hybrid movie recommendation system

movielens

4 different recommendation engines for the MovieLens dataset.

movielens-recommender

A pure Python implement of Collaborative Filtering based on MovieLens' dataset.

movierecommendation

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。

movierecommendation-1

UserCF和ItemCF协同过滤推荐算法的实现

mywebserver

Tiny WebServer Based on Reactor Model 基于Reactor模式的高效WebServer

neo4j-python-pandas-py2neo-v3

利用pandas将excel中数据抽取，以三元组形式加载到neo4j数据库中构建相关知识图谱

neteasecloudmusicapi

网易云音乐 Node.js API service

neural-attentive-item-similarity-model

TensorFlow Implementation of Neural Attentive Item Similarity Model for Recommendation on TKDE 2018

personal_recommendation-master

The personalized recommendation system is an intelligent platform based on massive data mining. It can simulate store sales personnel to provide product information and suggestions to customers, and provide fully personalized decision support and information services for customers' shopping. Its goal is to Satisfying the needs of users, meeting the needs that users are not aware of, or realizing, but not expressing the needs, allowing users to go beyond the individual's vision and avoid seeing the trees without seeing the forest. A good recommendation system can greatly increase user loyalty and bring huge benefits to e-commerce. Personalized recommendation is to recommend information and products of interest to users according to their interests and purchasing behavior. As the scale of e-commerce continues to expand, the number and variety of products grow rapidly, and customers need to spend a lot of time to find the products they want to buy. This kind of browsing of a large amount of unrelated information and product processes will undoubtedly cause consumers who are drowning in information overload problems to continue to lose. In order to solve these problems, a personalized recommendation system came into being. The recommendation system is a branch of data mining. It is a special data mining system, which is mainly reflected in the real-time and interactivity of the recommendation system. The system recommends information that meets the interests of the user according to the user's interests, also known as the personalized recommendation system. It not only based on the user's past history, but also needs to react in real time with the behavior of the current period of time, and correct and optimize the recommendation result according to the feedback result of interaction with the user.

pgpr

Reinforcement Knowledge Graph Reasoning for Explainable Recommendation

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