Papers with Code 아카이브

Collaborative Filtering

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크

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평가표

4

보존된 평가 기록

30

코드

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

https://github.com/fbenites/sklearn-hierarchical-classification

Feature-Weighted Linear Stacking

https://github.com/fdb78/NCF_recommender_systems

Neural Collaborative Filtering

https://github.com/fleanend/NeuralRecommender

GLocal-K: Global and Local Kernels for Recommender Systems

https://github.com/fleanend/TorchGlocalK

GLocal-K: Global and Local Kernels for Recommender Systems

https://github.com/florianeBhz/NGCF_Pytorch

Neural Graph Collaborative Filtering

https://github.com/fotol1/recbaselines2023

NAIS: Neural Attentive Item Similarity Model for Recommendation

https://github.com/franckjay/TorchEASE

Embarrassingly Shallow Autoencoders for Sparse Data

https://github.com/fukatani/stacked_generalization

Feature-Weighted Linear Stacking

https://github.com/fumiyo0607/KGAN

Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

https://github.com/giantonia/KGATv2

Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

https://github.com/gimys/recommeder_system

Neural Collaborative Filtering

https://github.com/glami/sansa

Embarrassingly Shallow Autoencoders for Sparse Data

https://github.com/globality-corp/sklearn-hierarchical-classification

Feature-Weighted Linear Stacking

https://github.com/gsq7474741/Paddle-NCR

Neural Collaborative Reasoning

https://github.com/guoyang9/NCF

Neural Collaborative Filtering

https://github.com/gusye1234/LightGCN-PyTorch

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

https://github.com/hankchau/rec_systems

Neural Collaborative Filtering

https://github.com/hasteck/EDLAE_NeurIPS2020

Variational Autoencoders for Collaborative Filtering

https://github.com/hengruizhang98/GCMC-Pytorch-dgl

Graph Convolutional Matrix Completion

https://github.com/hero1455/mindspore_models/tree/main/ncf

Neural Collaborative Filtering

https://github.com/hidasib/gru4rec_pytorch_official

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

https://github.com/hidasib/gru4rec_tensorflow_official

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

https://github.com/hoonose/robust-filter

Robust PCA via Outlier Pursuit

https://github.com/hsientzucheng/MKR.PyTorch

Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation

https://github.com/huangtinglin/NGCF-PyTorch

Neural Graph Collaborative Filtering

https://github.com/hungpthanh/gru4rec-pytorch

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

https://github.com/hungthanhpham94/GRU4REC-pytorch

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

https://github.com/hyunbool/Frequent-Pattern-Mining-using-Language-Model

Item2Vec: Neural Item Embedding for Collaborative Filtering

https://github.com/hyunbool/frequent_itemset_mining_using_language_model

Item2Vec: Neural Item Embedding for Collaborative Filtering

https://github.com/hyunbool/item2vec_movie_practice

Item2Vec: Neural Item Embedding for Collaborative Filtering

https://github.com/hyunbool/item_recommender_with_nlp

Item2Vec: Neural Item Embedding for Collaborative Filtering

https://github.com/icyeyeball/Peitent

Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario

https://github.com/immabe/NGCF_pytorch

Neural Graph Collaborative Filtering

https://github.com/informagi/AQA

A Contextual-Bandit Approach to Personalized News Article Recommendation

https://github.com/irskid5/drr_restaurants

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

https://github.com/jacobmunson/lenskit_confidence

LensKit for Python: Next-Generation Software for Recommender System Experiments

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