Papers with Code 아카이브

Collaborative Filtering

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보존된 평가 기록

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Machine-Learning-for-Everyone-ML4E/CoP-Code-of-Papers

Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing

https://github.com/MadPr0grammer/CF_Project

Indian Regional Movie Dataset for Recommender Systems

https://github.com/MiloudBi/NGCF-pytorch

Neural Graph Collaborative Filtering

https://github.com/Mind23-2/MindCode-4/tree/main/bgcf

Towards Representation Alignment and Uniformity in Collaborative Filtering

https://github.com/Mind23-2/MindCode-4/tree/main/gat

DGCN: Diversified Recommendation with Graph Convolutional Networks

https://github.com/MindCode-4/code-10/tree/main/DiffNet%2B%2B

DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation

https://github.com/MindCode-4/code-14/tree/main/DiffNet%2B%2B

DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation

https://github.com/MindSpore-scientific/code-10/tree/main/shallow-rnns

Embarrassingly Shallow Autoencoders for Sparse Data

https://github.com/NamCyan/RecSys-VAE4CF

Variational Autoencoders for Collaborative Filtering

https://github.com/NeverInAsh/fit-recommendation

A Deep Learning System for Predicting Size and Fit in Fashion E-Commerce

https://github.com/OweysMomenzada/Graph-Neural-Networks-for-effecient-Recommender-Systems

Graph Convolutional Matrix Completion

https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/deeprec

Training Deep AutoEncoders for Collaborative Filtering

https://github.com/PaddlePaddle/PaddleRec/tree/release/2.1.0/models/recall/ncf

Neural Collaborative Filtering

https://github.com/PeiJieSun/NESCL

Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering

https://github.com/PreferredAI/cornac

Graph Convolutional Matrix Completion

https://github.com/PreferredAI/cornac/tree/master/cornac/models/vaecf

Variational Autoencoders for Collaborative Filtering

https://github.com/ROpdam/recommender_comparison

Collaborative Filtering with Recurrent Neural Networks

https://github.com/Rec4Fun/DisCo_WWW_25

Personalized Diffusion Model Reshapes Cold-Start Bundle Recommendation

https://github.com/Recvani/benchmark

Hybrid Recommender System based on Autoencoders

https://github.com/SAI990323/BIGRec

A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems

https://github.com/SJD1882/Big-Data-Recommender-Systems

Hybrid Recommender System based on Autoencoders

https://github.com/Shaked35/fairness-optimization

Beyond Parity: Fairness Objectives for Collaborative Filtering

https://github.com/SharonLSY/CDRC-MSc---Recommender-Systems

Recurrent Neural Networks with Top-k Gains for Session-based Recommendations

https://github.com/SharonLSY/CDRC-MSc-Recommender-Systems

Recurrent Neural Networks with Top-k Gains for Session-based Recommendations

https://github.com/Shivanandmn/Knowledge-Tracing-SAINT

Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing

https://github.com/Shivanandmn/SAINT_plus-Knowledge-Tracing-

Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing

https://github.com/SirRob1997/Collaborative-Filtering-under-Model-Uncertainty

Collaborative Filtering under Model Uncertainty

https://github.com/TenglunTan/Multi-armed-bandits

A Contextual-Bandit Approach to Personalized News Article Recommendation

https://github.com/Tingting2477/DGCF_torch

Disentangled Graph Collaborative Filtering

https://github.com/Trantin84/NGCF-Tin

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

https://github.com/UlionTse/mlgb

Neural Collaborative Filtering

https://github.com/UnibucProjects/DeepRLRecommenderSystem

Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling

https://github.com/UnknownUnit/Bandits_project

A Contextual-Bandit Approach to Personalized News Article Recommendation

https://github.com/WANGBohaO-jpg/MSL

A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems

https://github.com/Wuyxin/LightGCN-parallelized-version

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

https://github.com/ZJJHYM/RippleNet

RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.