https://github.com/roger-zhe-li/wsdm21-mainstream
공식 코드Leave No User Behind: Towards Improving the Utility of Recommender Systems for Non-mainstream Users
Papers with Code 아카이브
Collaborative Filtering
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/rsalganik1123/larp
공식 코드LARP: Language Audio Relational Pre-training for Cold-Start Playlist Continuation
https://github.com/rsheth80/pmf-automl
공식 코드Probabilistic Matrix Factorization for Automated Machine Learning
https://github.com/rucaibox/cogcl
공식 코드Enhancing Graph Contrastive Learning with Reliable and Informative Augmentation for Recommendation
https://github.com/rucaibox/ncl
공식 코드Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning
https://github.com/ruizhang-ai/GMCF_Neural_Graph_Matching_based_Collaborative_Filtering
공식 코드https://github.com/rutgerswiselab/CCF
공식 코드https://github.com/rutgerswiselab/DCCF
공식 코드https://github.com/rutgerswiselab/DyCCF
공식 코드https://github.com/rutgerswiselab/NCR
공식 코드https://github.com/saeedkhaki92/collaborative-filtering-for-yield-prediction
공식 코드Predicting Yield Performance of Parents in Plant Breeding: A Neural Collaborative Filtering Approach
https://github.com/sagarverma/LC-CFRBM
공식 코드Collaborative Filtering with Label Consistent Restricted Boltzmann Machine
https://github.com/sai990323/grounding4rec
공식 코드A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems
https://github.com/samikhenissi/TheoretUserModeling
공식 코드https://github.com/samlobel/RaCT_CF
공식 코드Towards Amortized Ranking-Critical Training for Collaborative Filtering
https://github.com/savvinadaniil/enrichbookcrossing
공식 코드https://github.com/sckangz/CIKM16
공식 코드https://github.com/secure-ai-assistants/norm-prediction
공식 코드https://github.com/seongku-kang/concf_www22
공식 코드Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering
https://github.com/sgjimenezv/star_trek_recsys
공식 코드An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise
https://github.com/shoujin88/graph-learning-based-recommender-systems-GLRS-
공식 코드https://github.com/sileod/language-model-recommendation
공식 코드https://github.com/sisinflab/DatasetsSplits
공식 코드https://github.com/sisinflab/Reenvisioning-the-comparison-between-Neural-Collaborative-Filtering-and-Matrix-Factorization
공식 코드Reenvisioning Collaborative Filtering vs Matrix Factorization
https://github.com/sisinflab/adversarial-recommender-systems-survey
공식 코드https://github.com/sisinflab/graph-characteristics
공식 코드https://github.com/sisinflab/graph-rss-reproducibility
공식 코드https://github.com/sisinflab/kguf
공식 코드KGUF: Simple Knowledge-aware Graph-based Recommender with User-based Semantic Features Filtering
https://github.com/sisinflab/recommenders
공식 코드https://github.com/sliu675/autoseqrec
공식 코드AutoSeqRec: Autoencoder for Efficient Sequential Recommendation
https://github.com/snap-research/test-time-aggregation-for-cf
공식 코드https://github.com/socialrecsys/smin
공식 코드Social Recommendation with Self-Supervised Metagraph Informax Network
https://github.com/song3134/CVRCF
공식 코드https://github.com/spacelearner/jgcf
공식 코드On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering
https://github.com/stanis-morozov/prodige
공식 코드Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs
https://github.com/statusrank/LibCML
공식 코드The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.