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/apd10/flcf

공식 코드

Heterogeneous federated collaborative filtering using FAIR: Federated Averaging in Random Subspaces

https://github.com/apple/ml-sad

공식 코드

Consistent Collaborative Filtering via Tensor Decomposition

https://github.com/applied-machine-learning-lab/embedding-in-recommender-systems

공식 코드

Embedding in Recommender Systems: A Survey

https://github.com/applied-machine-learning-lab/llmemb

공식 코드

LLMEmb: Large Language Model Can Be a Good Embedding Generator for Sequential Recommendation

https://github.com/arthurma71/advdrop

공식 코드

General Debiasing for Graph-based Collaborative Filtering via Adversarial Graph Dropout

https://github.com/arthurmensch/modl

공식 코드

Dictionary Learning for Massive Matrix Factorization

https://github.com/aryan-jadon/evaluation-metrics-for-recommendation-systems

공식 코드

A Comprehensive Survey of Evaluation Techniques for Recommendation Systems

https://github.com/asa9aotk/ans-recbole

공식 코드

Augmented Negative Sampling for Collaborative Filtering

https://github.com/awslabs/language-model-prior-4-item-cold-start

공식 코드

Language-Model Prior Overcomes Cold-Start Items

https://github.com/bahareAskari/JoVA-Hinge

공식 코드

Joint Variational Autoencoders for Recommendation with Implicit Feedback

https://github.com/beilineili/BCFNet

공식 코드

BCFNet: A Balanced Collaborative Filtering Network with Attention Mechanism

https://github.com/big-data-lab-team/paper-pipelines-datasets-recommender

공식 코드

A Recommender System for Scientific Datasets and Analysis Pipelines

https://github.com/big-data-lab-team/paper-reproducibility-collaborative-filtering

공식 코드

Predicting computational reproducibility of data analysis pipelines in large population studies using collaborative filtering

https://github.com/bigshawne/csci_544_final_proj_recommendation_system_based_on_nlp

공식 코드

Given Users Recommendations Based on Reviews on Yelp

https://github.com/blueghostyi/id-grec

공식 코드

Unveiling Contrastive Learning's Capability of Neighborhood Aggregation for Collaborative Filtering

https://github.com/broadrs/broadcf

공식 코드

Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach

https://github.com/bt-nghia/disco

공식 코드

Personalized Diffusion Model Reshapes Cold-Start Bundle Recommendation

https://github.com/buds-lab/recommender-sys-for-buildings-textmining-review

공식 코드

Recommender systems and reinforcement learning for human-building interaction and context-aware support: A text mining-driven review of scientific literature

https://github.com/casperhansen/NeuHash-CF

공식 코드

Content-aware Neural Hashing for Cold-start Recommendation

https://github.com/casperhansen/Projected-Hamming-Dissimilarity

공식 코드

Projected Hamming Dissimilarity for Bit-Level Importance Coding in Collaborative Filtering

https://github.com/chanwoo0806/chebycf

공식 코드

Graph Spectral Filtering with Chebyshev Interpolation for Recommendation

https://github.com/chaue/mf-algorithms

공식 코드

On Application of Block Kaczmarz Methods in Matrix Factorization

https://github.com/chengkai-liu/FlowCF

공식 코드

Flow Matching for Collaborative Filtering

https://github.com/chengkai-liu/TwinCL

공식 코드

TwinCL: A Twin Graph Contrastive Learning Model for Collaborative Filtering

https://github.com/chenxing1999/recsys-benchmark

공식 코드

A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender Systems

https://github.com/cheungdaven/semi-ae-recsys

공식 코드

Hybrid Collaborative Recommendation via Semi-AutoEncoder

https://github.com/chrystalii/cngcf

공식 코드

Neural Causal Graph Collaborative Filtering

https://github.com/clhchtcjj/BiNE

공식 코드

Learning Vertex Representations for Bipartite Networks

https://github.com/cod3licious/simec

공식 코드

Predicting Pairwise Relations with Neural Similarity Encoders

https://github.com/cosanlab/collab_filter

공식 코드

Recovering individual emotional states from sparse ratings using collaborative filtering

https://github.com/cpjku/adv-multvae

공식 코드

Unlearning Protected User Attributes in Recommendations with Adversarial Training

https://github.com/cpu135/item-graph2vec

공식 코드

Item-Graph2vec: a Efficient and Effective Approach using Item Co-occurrence Graph Embedding for Collaborative Filtering

https://github.com/cripac-dig/balanced-multimodal-rec

공식 코드

Modality-Balanced Learning for Multimedia Recommendation

https://github.com/cripac-dig/micro

공식 코드

Latent Structure Mining with Contrastive Modality Fusion for Multimedia Recommendation

https://github.com/cuMF/cumf_als

공식 코드

Matrix Factorization on GPUs with Memory Optimization and Approximate Computing

https://github.com/cuiqiang1990/MV-RNN

공식 코드

MV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation

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