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aldi

This is our implementation for ALDI.

base_code

ccfcrec

clcrec

Contrastive Learning for Cold-start Recommendation

conditional-variational-autoencoder-recommendation

PyTorch implementation of our paper "Improving Item Cold-start Recommendation via Model-agnostic Conditional Variational Autoencoder" accepted by SIGIR 2022.

conv_arithmetic

A technical report on convolution arithmetic in the context of deep learning

cpr

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

dccf

[SIGIR'2023] "DCCF: Disentangled Contrastive Collaborative Filtering"

dragon

finch-clustering

Source Code for FINCH Clustering Algorithm

gnn-recommender-systems

An index of recommendation algorithms that are based on Graph Neural Networks.

heater--cold-start-recommendation

Code for the SIGIR20 paper -- Recommendation for New Users and New Items via Randomized Training and Mixture-of-Experts Transformation

invrl

The official implementation of InvRL

jtcn

lgmrec

[AAAI 2024] Official Pytorch Implementation for "LGMRec: Local and Global Graph Learning for Multimodal Recommendation".

lightgcn

mgat

This project is the source code for Multimodal Graph Attention Network(MGAT). The source code will be available on the acceptance of the paper.

micro

mmgcn

MMGCN: Multi-modal Graph Convolution Network forPersonalized Recommendation of Micro-video

mmssl

[WWW'2023] "MMSSL: Multi-Modal Self-Supervised Learning for Recommendation"

monet

mtpr

Experimental codes for the paper 'How to Learn Item Representation for Cold-Start Multimedia Recommendation?'

neuhash-cf

Content-aware Neural Hashing for Cold-start Recommendation. SIGIR 2020

neural_collaborative_filtering

Neural Collaborative Filtering

neural_graph_collaborative_filtering

Neural Graph Collaborative Filtering, SIGIR2019

simclr

A PyTorch implementation of SimCLR based on ICML 2020 paper "A Simple Framework for Contrastive Learning of Visual Representations"

simclr-office

SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners

source-code-notebook

关于一些经典论文源码的逐行中文笔记