hexiangnan

recommenders

Best Practices on Recommendation Systems

neural_collaborative_filtering

Neural Collaborative Filtering

NeuRec

Next RecSys Library

knowledge_graph_attention_network

KGAT: Knowledge Graph Attention Network for Recommendation, KDD2019

neural_graph_collaborative_filtering

Neural Graph Collaborative Filtering, SIGIR2019

LightGCN

KPRN

Reasoning Over Knowledge Graph Paths for Recommendation

joint-kg-recommender

TALLRec

ConvNCF

Experimental codes for paper "Outer Product-based Neural Collaborative Filtering".

GAAL-based-outlier-detection

GAAL-based Outlier Detection

DIR-GNN

Official code of "Discovering Invariant Rationales for Graph Neural Networks" (ICLR 2022)

WSDM2019-nextitnet

Generative model for sequential recommendation based on Convolution Neural Networks (CNN))

DeepICF

TensorFlow Implementation of Deep Item-based Collaborative Filtering Model for Top-N Recommendation

TIFUKNN

kNN-based next-basket recommendation

MACR

[KDD 2021] Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System

lambda-opt

Pytorch implementation of λOpt: Learn to Regularize Recommender Models in Finer Levels, KDD 2019

CFM

RCF

Tensorflow implementation of RCF

CPR

TensorFlow implementation of our paper: Cross Pairwise Ranking for Unbiased Item Recommendation (WWW'22)

ReinforceNS

AMR

This is our official implementation for the paper: Jinhui Tang, Xiaoyu Du, Xiangnan He, Fajie Yuan, Qi Tian, and Tat-Seng Chua, Adversarial Training Towards Robust Multimedia Recommender System.

Attentive-Aspect-based-Recommendation-Model

Sets2Sets

Sequential sets to sequential sets learning

IntroDaSE

《数据科学与工程导论》教材配套资源

Group-Contextualization

[CVPR22] Group Contextualization for Video Recognition

DCR

This is an implementation for our paper "Addressing Confounding Feature Issue for Causal Recommendation" based on PyTorch.

TIDE

Cross-GCN

Cross-GCN: Enhancing Graph Convolutional Network with k-Order Feature Interactions

AFISM

APR enhances the pairwise ranking method BPR by performing adversarial training. To illustrate how it works, APR on FISM is implemented here by adding adversarial perturbations on the matrices P and Q.