Papers with Code 아카이브

Collaborative Filtering

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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/newlei/LR-GCCF

공식 코드

Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach

https://github.com/newlei/LRGCCF

공식 코드

Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach

https://github.com/newlei/Set2setRank

공식 코드

Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation

https://github.com/newlei/fda

공식 코드

Improving Recommendation Fairness via Data Augmentation

https://github.com/nicholasjohnson2020/sparselowranksoftware

공식 코드

Sparse Plus Low Rank Matrix Decomposition: A Discrete Optimization Approach

https://github.com/nju-websoft/RGRec

공식 코드

Rule-Guided Graph Neural Networks for Recommender Systems

https://github.com/nokiroki/recsys-evaluation-of-dozens-datasets

공식 코드

From Variability to Stability: Advancing RecSys Benchmarking Practices

https://github.com/noveens/sampling_cf

공식 코드

On Sampling Collaborative Filtering Datasets

https://github.com/nzhinusoftcm/review-on-collaborative-filtering

공식 코드

A Comprehensive Review on Non-Neural Networks Collaborative Filtering Recommendation Systems

https://github.com/oceanlvr/protoau

공식 코드

Prototypical Contrastive Learning through Alignment and Uniformity for Recommendation

https://github.com/overwenyan/joint-search

공식 코드

Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering

https://github.com/pdturney/multislice-projection

공식 코드

Empirical Evaluation of Four Tensor Decomposition Algorithms

https://github.com/peiyance/transgnn-torch

공식 코드

TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems

https://github.com/pepsi2222/cela

공식 코드

CELA: Cost-Efficient Language Model Alignment for CTR Prediction

https://github.com/pmuellner/impactofdp

공식 코드

The Impact of Differential Privacy on Recommendation Accuracy and Popularity Bias

https://github.com/psm1206/dan

공식 코드

Why is Normalization Necessary for Linear Recommenders?

https://github.com/psm1206/mawu

공식 코드

Toward a Better Understanding of Loss Functions for Collaborative Filtering

https://github.com/psywaves/EVCF

공식 코드

Enhancing VAEs for Collaborative Filtering: Flexible Priors & Gating Mechanisms

https://github.com/putatu/dataleakagerec

공식 코드

A Critical Study on Data Leakage in Recommender System Offline Evaluation

https://github.com/qitianwu/IDCF

공식 코드

Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach

https://github.com/quanmingyao/SIF

공식 코드

Efficient Neural Interaction Function Search for Collaborative Filtering

https://github.com/quentin-duchemin/inference-markovian-SBM

공식 코드

Reliable Time Prediction in the Markov Stochastic Block Model

https://github.com/rafaelppires/rex

공식 코드

TEE-based decentralized recommender systems: The raw data sharing redemption

https://github.com/rahmanidashti/cpfairrank

공식 코드

A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systems

https://github.com/rdevooght/MF-with-prior-and-updates

공식 코드

Dynamic Matrix Factorization with Priors on Unknown Values

https://github.com/rdevooght/sequence-based-recommendations

공식 코드

Collaborative Filtering with Recurrent Neural Networks

https://github.com/recombee/beeformer

공식 코드

beeFormer: Bridging the Gap Between Semantic and Interaction Similarity in Recommender Systems

https://github.com/recsys-lab/rag-visualrec

공식 코드

RAG-VisualRec: An Open Resource for Vision- and Text-Enhanced Retrieval-Augmented Generation in Recommendation

https://github.com/recsyspolimi/ecir-2022-an-evaluation-of-gan-for-cf

공식 코드

An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering

https://github.com/reczoo/RecZoo/tree/main/matching/cf/SimpleX

공식 코드

SimpleX: A Simple and Strong Baseline for Collaborative Filtering

https://github.com/reczoo/RecZoo/tree/main/matching/gnn/UltraGCN

공식 코드

UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation

https://github.com/riannevdberg/gc-mc

공식 코드

Graph Convolutional Matrix Completion

https://github.com/riktor/exadmm-recommender

공식 코드

Scalable and Provably Fair Exposure Control for Large-Scale Recommender Systems

https://github.com/riktor/safer2-recommender

공식 코드

Safe Collaborative Filtering

https://github.com/riwei-heu/ahns

공식 코드

Adaptive Hardness Negative Sampling for Collaborative Filtering

https://github.com/rn5l/session-rec

공식 코드

A Survey on Session-based Recommender Systems

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