Papers with Code 아카이브

Collaborative Filtering

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전체 기법전체 데이터셋

하위 태스크

0

평가표

4

보존된 평가 기록

30

코드

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

https://github.com/sisinflab/TimePOP

Local Popularity and Time in top-N Recommendation

https://github.com/skp1999/Recommender-Systems---Neural-Collaborative-Filtering

Neural Collaborative Filtering

https://github.com/soycoder/dlrm-fb

Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems

https://github.com/srendle/libfm

On the Difficulty of Evaluating Baselines: A Study on Recommender Systems

https://github.com/sriram7777/recommendation_engine

Neural Collaborative Filtering

https://github.com/stabuev/d2l-ru

Learning User Representations with Hypercuboids for Recommender Systems

https://github.com/stillarrow/SeqRec

Neural Collaborative Filtering

https://github.com/strin/pyOptSpace

A Gradient Descent Algorithm on the Grassman Manifold for Matrix Completion

https://github.com/sumitsidana/NERvE

Representation Learning and Pairwise Ranking for Implicit Feedback in Recommendation Systems

https://github.com/suvigyavijay/DeepRecommender

Training Deep AutoEncoders for Collaborative Filtering

https://github.com/swethmandava/scaleDL_ghc19

Neural Collaborative Filtering

https://github.com/swtheing/Multiview-Link-Representation-Learning

Graph Convolutional Matrix Completion

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

Neural Graph Collaborative Filtering

https://github.com/tanimutomo/gcmc

Graph Convolutional Matrix Completion

https://github.com/tanya525625/LightGCN-PyTorch

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

https://github.com/tezignlab/RippleNet-TF2

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

https://github.com/thanhdtran/SDMR

Neural Collaborative Filtering

https://github.com/tiagodscunha/cf2vec

Algorithm Selection for Collaborative Filtering: the influence of graph metafeatures and multicriteria metatargets

https://github.com/tiagodscunha/cf_metafeatures

Algorithm Selection for Collaborative Filtering: the influence of graph metafeatures and multicriteria metatargets

https://github.com/tiagodscunha/lr_alg_sel

Algorithm Selection for Collaborative Filtering: the influence of graph metafeatures and multicriteria metatargets

https://github.com/tjcdev/gcmc-tf2

Graph Convolutional Matrix Completion

https://github.com/tobiasweede/rs-via-gnn

Graph Convolutional Matrix Completion

https://github.com/tohtsky/myFM

On the Difficulty of Evaluating Baselines: A Study on Recommender Systems

https://github.com/tubamuzzaffar/g

Graph Convolutional Matrix Completion

https://github.com/tubamuzzaffar/gc

Graph Convolutional Matrix Completion

https://github.com/tubamuzzaffar/gc2

Graph Convolutional Matrix Completion

https://github.com/tubamuzzaffar/gcmc-2

Graph Convolutional Matrix Completion

https://github.com/vincizhu/giffcf

Graph Signal Diffusion Model for Collaborative Filtering

https://github.com/vipin14119/Recommender_System_AutoEncoder

Indian Regional Movie Dataset for Recommender Systems

https://github.com/weiyinwei/huign

Neural Graph Collaborative Filtering

https://github.com/weiyinwei/mmgcn

Neural Graph Collaborative Filtering

https://github.com/wujcan/sgl-torch

Self-supervised Graph Learning for Recommendation

https://github.com/xiangwang1223/knowledge_graph_attention_network

Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

https://github.com/xiaoleiHou214/BGCF-pytorch-master

Neural Graph Collaborative Filtering

https://github.com/xiaoleiHou214/gc-mc-master

Graph Convolutional Matrix Completion

https://github.com/xiawenwen49/NCF

Neural Collaborative Filtering

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