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Collaborative Filtering
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/OpenISS/kg-recommendation-framework
공식 코드Application of Knowledge Graphs to Provide Side Information for Improved Recommendation Accuracy
https://github.com/PASSIONLab/distributed_sddmm
공식 코드https://github.com/PeiJieSun/diffnet
공식 코드A Neural Influence Diffusion Model for Social Recommendation
https://github.com/RUCAIBox/RecBole
공식 코드RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms
https://github.com/RuijiaW/Multi-Component-Graph-Convolutional-Collaborative-Filtering
공식 코드https://github.com/SirRob1997/collaborative_filtering
공식 코드https://github.com/Slowika/Bag-of-Recipes
공식 코드Completing partial recipes using item-based collaborative filtering to recommend ingredients
https://github.com/TUMLegalTech/bva-citation-prediction
공식 코드Context-Aware Legal Citation Recommendation using Deep Learning
https://github.com/TengShi-RUC/CFRAG
공식 코드Retrieval Augmented Generation with Collaborative Filtering for Personalized Text Generation
https://github.com/Wenhui-Yu/TDAR
공식 코드Semi-supervised Collaborative Filtering by Text-enhanced Domain Adaptation
https://github.com/WuYunfan/igcn_cf
공식 코드INMO: A Model-Agnostic and Scalable Module for Inductive Collaborative Filtering
https://github.com/XinGla/RCF
공식 코드Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation
https://github.com/Xinrui17/CoFedRec
공식 코드https://github.com/ZAL-NASK/CSMC
공식 코드https://github.com/adolfo-almeida/scaled_cer
공식 코드https://github.com/adxcreative/LEARN
공식 코드https://github.com/airtownapp/airtown-application
공식 코드Use of Air Quality Sensor Network Data for Real-time Pollution-Aware POI Suggestion
https://github.com/akaxlh/cranet
공식 코드Collaborative Reflection-Augmented Autoencoder Network for Recommender Systems
https://github.com/akaxlh/gnmr
공식 코드Multi-Behavior Enhanced Recommendation with Cross-Interaction Collaborative Relation Modeling
https://github.com/akaxlh/hccf
공식 코드https://github.com/akaxlh/sht
공식 코드Self-Supervised Hypergraph Transformer for Recommender Systems
https://github.com/albmlpy/dynamiccf
공식 코드Dynamic Collaborative Filtering for Matrix- and Tensor-based Recommender Systems
https://github.com/alex-cse/NCF_AHSTD
공식 코드Neural Collaborative Filtering to Detect Anomalies in Human Semantic Trajectories
https://github.com/alicogintel/SDM
공식 코드SDM: Sequential Deep Matching Model for Online Large-scale Recommender System
https://github.com/alirezagharahi/d_SBRS
공식 코드https://github.com/amoussawi/recoder
공식 코드Towards Large Scale Training Of Autoencoders For Collaborative Filtering
https://github.com/andreakiro/cil-lab
공식 코드https://github.com/andrebola/icassp2021
공식 코드Melon Playlist Dataset: a public dataset for audio-based playlist generation and music tagging
https://github.com/anonyfeb/dsml
공식 코드Discrete Scale-invariant Metric Learning for Efficient Collaborative Filtering
https://github.com/anonymous-research-entity/recsys2022-cqfs-ttopt
공식 코드https://github.com/anonymouspap/latte_recsys
공식 코드Tensor-based Collaborative Filtering With Smooth Ratings Scale
https://github.com/antoniopurificato/sheaf4rec
공식 코드Sheaf4Rec: Sheaf Neural Networks for Graph-based Recommender Systems
https://github.com/anzhang314/bc-loss
공식 코드Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering
https://github.com/anzhang314/invcf
공식 코드Invariant Collaborative Filtering to Popularity Distribution Shift
https://github.com/anzhang314/popgo
공식 코드Robust Collaborative Filtering to Popularity Distribution Shift
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.