AAAI-2021 《Knowledge-aware Coupled Graph Neural Network for Social Recommendation》
train model:
Dataset: Yelp, Result: test HR = 0.8026, test NDCG = 0.5326
python main.py --dataset Yelp --reg 0.05 --lr 0.01 --batch 2048 --hide_dim 64 --layer [64] --slope 0.4 --time_step 360 --lam [0.1,0.001] --clear 1 --fuse mean
recommender Movies/TV Recommender. Recommends Movies, TV Shows, Actors, Directors, Writers. Setup Create file API_KEY and paste your TMDB API key in i
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
Crab - A Python Library for Recommendation Engines
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
RecSim NG, a probabilistic platform for multi-agent recommender systems simulation. RecSimNG is a scalable, modular, differentiable simulator implemented in Edward2 and TensorFlow. It offers: a power
Implementation-of-a-hadoop-based-movie-recommendation-system 通过编写代码，设计一个基于Hadoop的电影推荐系统，通过此推荐系统的编写，掌握在Hadoop平台上的文件操作，数据处理的技能。windows 10 hadoop 2.8.3 p
This project will access your last listened songs on Spotify using its API, then it will request the user to select 5 favorite songs in that list, on which the API will proceed to make 50 recommendat
Movie-Recommender-System Movie-Recommender-System is a web application using which a user can select his/her watched movie from list and system will r
MKM-SR Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation Paper data and code This is the
Included Conferences: SIGIR 2020, SIGKDD 2020, RecSys 2020, CIKM 2020, AAAI 2021, WSDM 2021, WWW 2021
GCE-GNN Code This is the source code for SIGIR 2020 Paper: Global Context Enhanced Graph Neural Networks for Session-based Recommendation. Requirement
MB-GMN Code for MB-GMN, SIGIR 2021 For Beibei data, run python .\labcode.py For Tmall data, run python .\labcode.py --data tmall --rank 2 For IJCAI
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
Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation
RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation Pytorch based implemention of Relational Temporal
Detecting Beneficial Feature Interactions for Recommender Systems (L0-SIGN) This is our implementation for the paper: Su, Y., Zhang, R., Erfani, S., &
Recommender systems are the systems that are designed to recommend things to the user based on many different factors. The recommender system deals with a large volume of information present by filte
Respiratory-Health-Recommendation-System Respiratory Health Recommendation System based on Air Quality Index Forecasts This project aims to provide pr
Bundle Graph Convolutional Network This is our Pytorch implementation for the paper: Jianxin Chang, Chen Gao, Xiangnan He, Depeng Jin and Yong Li. Bun
Overview Surprise is a Python scikit for building and analyzing recommender systems that deal with explicit rating data. Surprise was designed with th
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