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/nnkkmto/item2vec-rec-test

Item2Vec: Neural Item Embedding for Collaborative Filtering

https://github.com/noob-life/VAE-for-Collaborative-Filtering-Pytorch

Variational Autoencoders for Collaborative Filtering

https://github.com/nwuAI/Circular-Neural-Network-With-Time-Perception

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

https://github.com/oliviaxyl/A-Simple-Movie-Recommender-System-to-Classmates-

Neural Collaborative Filtering

https://github.com/onurboyar/RecommenderSystems

Variational Autoencoders for Collaborative Filtering

https://github.com/otto-de/recsys-dataset

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

https://github.com/parno/autobid

StarSpace: Embed All The Things!

https://github.com/paryanasr/HonoursProject-Recommender-System-gc-mc

Graph Convolutional Matrix Completion

https://github.com/pcerdam/KerasGRU4Rec

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

https://github.com/philippe-gagne/treasure-boxd

Dual-embedding based Neural Collaborative Filtering for Recommender Systems

https://github.com/phucdkbk/recommendation-tensorflow-2

NAIS: Neural Attentive Item Similarity Model for Recommendation

https://github.com/pyy0715/Neural-Collaborative-Filtering

Neural Collaborative Filtering

https://github.com/pyy0715/vae-cf-pyorch

Variational Autoencoders for Collaborative Filtering

https://github.com/qbxlvnf11/hmlet

Linear, or Non-Linear, That is the Question!

https://github.com/qibinc/RippleNet-PyTorch

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

https://github.com/rajashah33/new-fit-recomm

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

https://github.com/rashmi1112/Reccomendation-System-for-E-Commerce-Application

Hybrid Recommender Systems: A Systematic Literature Review

https://github.com/ray-project/ray/tree/master/rllib

A Contextual-Bandit Approach to Personalized News Article Recommendation

https://github.com/recsys-benchmark/daisyrec-v2.0

Embarrassingly Shallow Autoencoders for Sparse Data

https://github.com/recsyspolimi/an-evaluation-of-GAN-for-CF

An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering

https://github.com/rfenzo/ProyectoRecomendadores

Collaborative Filtering and the Missing at Random Assumption

https://github.com/rouxiaoxiao/CMNPlus

Collaborative Memory Network for Recommendation Systems

https://github.com/rowantseng/DeepCF-PyTorch

DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System

https://github.com/rushhan/VAE-for-Collaborative-Filtering-Pytorch

Variational Autoencoders for Collaborative Filtering

https://github.com/samiwilf/dlrm_from_shz0116

Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems

https://github.com/savvinadaniil/biasinrecommendation

Hidden Author Bias in Book Recommendation

https://github.com/sayamsingla2000/LightGCN_MovieLens

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

https://github.com/sb-ai-lab/RePlay

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

https://github.com/sciueferrara/fcf-huawei

Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System

https://github.com/sdu-wjh/icws2020

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

https://github.com/semihakbayrak/DeepLearning

Neural Collaborative Filtering

https://github.com/seoyoungh/svd-ae

SVD-AE: Simple Autoencoders for Collaborative Filtering

https://github.com/shashist/recsys-rl

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

https://github.com/shenweichen/deepmatch

Neural Collaborative Filtering

https://github.com/shuyao-wang/dsl

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

https://github.com/singhsidhukuldeep/Recommendation-System

Slope One Predictors for Online Rating-Based Collaborative Filtering

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