Papers with Code 아카이브

Collaborative Filtering

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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/git-smsun/dp-mf

공식 코드

Accelerating Matrix Factorization by Dynamic Pruning for Fast Recommendation

https://github.com/google-research/federated

공식 코드

Federated Reconstruction: Partially Local Federated Learning

https://github.com/google-research/federated/tree/master/reconstruction

공식 코드

Federated Reconstruction: Partially Local Federated Learning

https://github.com/google-research/google-research

공식 코드

Revisiting the Performance of iALS on Item Recommendation Benchmarks

https://github.com/google-research/google-research/tree/master/dot_vs_learned_similarity

공식 코드

Neural Collaborative Filtering vs. Matrix Factorization Revisited

https://github.com/gusye1234/pytorch-light-gcn

공식 코드

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

https://github.com/haoxuanli-pku/adrnet

공식 코드

ADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction

https://github.com/haoxuanli-pku/iclr24-kernel-balancing

공식 코드

Debiased Collaborative Filtering with Kernel-Based Causal Balancing

https://github.com/happypointer/llm2rec

공식 코드

LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential Recommendation

https://github.com/hasined/giffcf

공식 코드

Graph Signal Diffusion Model for Collaborative Filtering

https://github.com/hassounlab/boost-rs

공식 코드

Boost-RS: Boosted Embeddings for Recommender Systems and its Application to Enzyme-Substrate Interaction Prediction

https://github.com/hasteck/MRF_NeurIPS_2019

공식 코드

Markov Random Fields for Collaborative Filtering

https://github.com/hcai-mms/advx-multvae

공식 코드

Simultaneous Unlearning of Multiple Protected User Attributes From Variational Autoencoder Recommenders Using Adversarial Training

https://github.com/hcai-mms/sibrar---single-branch-recommender

공식 코드

A Multimodal Single-Branch Embedding Network for Recommendation in Cold-Start and Missing Modality Scenarios

https://github.com/herecj/fastvae_gpu

공식 코드

Fast Variational AutoEncoder with Inverted Multi-Index for Collaborative Filtering

https://github.com/hexiangnan/neural_collaborative_filtering

공식 코드

Neural Collaborative Filtering

https://github.com/hfutmars/RGCF

공식 코드

RGCF: Refined Graph Convolution Collaborative Filtering with concise and expressive embedding

https://github.com/hfutmars/megcf

공식 코드

MEGCF: Multimodal Entity Graph Collaborative Filtering for Personalized Recommendation

https://github.com/hhhhzy/nlu_project

공식 코드

Enhancing Collaborative Filtering Recommender with Prompt-Based Sentiment Analysis

https://github.com/hidasib/GRU4Rec

공식 코드

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

https://github.com/hieptk/accent

공식 코드

Counterfactual Explanations for Neural Recommenders

https://github.com/hipdac-lab/heat

공식 코드

HEAT: A Highly Efficient and Affordable Training System for Collaborative Filtering Based Recommendation on CPUs

https://github.com/hkuds/adagcl

공식 코드

Adaptive Graph Contrastive Learning for Recommendation

https://github.com/hkuds/autocf

공식 코드

Automated Self-Supervised Learning for Recommendation

https://github.com/hkuds/dccf

공식 코드

Disentangled Contrastive Collaborative Filtering

https://github.com/hkuds/easyrec

공식 코드

EasyRec: Simple yet Effective Language Models for Recommendation

https://github.com/hkuds/gformer

공식 코드

Graph Transformer for Recommendation

https://github.com/hkuds/gte

공식 코드

How Expressive are Graph Neural Networks in Recommendation?

https://github.com/hkuds/mixrec

공식 코드

MixRec: Heterogeneous Graph Collaborative Filtering

https://github.com/hkuds/reclm

공식 코드

RecLM: Recommendation Instruction Tuning

https://github.com/hkuds/simrec

공식 코드

Graph-less Collaborative Filtering

https://github.com/hkuds/sslrec

공식 코드

SSLRec: A Self-Supervised Learning Framework for Recommendation

https://github.com/hkuds/xrec

공식 코드

XRec: Large Language Models for Explainable Recommendation

https://github.com/hong-ming/scaledsgd

공식 코드

Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix Completion

https://github.com/hujunxianligong/MGDCF

공식 코드

MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering

https://github.com/hwwang55/KGCN

공식 코드

Knowledge Graph Convolutional Networks for Recommender Systems

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