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Collaborative Filtering

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하위 태스크

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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/hwwang55/MKR

공식 코드

Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation

https://github.com/hwwang55/RippleNet

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RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems

https://github.com/ilya-shenbin/RecVAE

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RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback

https://github.com/ilya-shenbin/implicitslim

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ImplicitSLIM and How it Improves Embedding-based Collaborative Filtering

https://github.com/jackfrost168/cf_diff

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Collaborative Filtering Based on Diffusion Models: Unveiling the Potential of High-Order Connectivity

https://github.com/jackmedda/fa4gcf

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Fair Augmentation for Graph Collaborative Filtering

https://github.com/jaewoong-lee/sigir_2023_uctrl

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uCTRL: Unbiased Contrastive Representation Learning via Alignment and Uniformity for Collaborative Filtering

https://github.com/jamesyifan/DisenHAN

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DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation

https://github.com/jd-opensource/ia-gcn

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IA-GCN: Interactive Graph Convolutional Network for Recommendation

https://github.com/jeongwhanchoi/HMLET

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Linear, or Non-Linear, That is the Question!

https://github.com/jeongwhanchoi/bspm

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Blurring-Sharpening Process Models for Collaborative Filtering

https://github.com/jeongwhanchoi/lt-ocf

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LT-OCF: Learnable-Time ODE-based Collaborative Filtering

https://github.com/jeongwhanchoi/scone

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SCONE: A Novel Stochastic Sampling to Generate Contrastive Views and Hard Negative Samples for Recommendation

https://github.com/jeongwhanchoi/svd-ae

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SVD-AE: Simple Autoencoders for Collaborative Filtering

https://github.com/jhliu0807/fair-sampling

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Unbiased Collaborative Filtering with Fair Sampling

https://github.com/jianlin-cheng/CATA

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CATA++: A Collaborative Dual Attentive Autoencoder Method for Recommending Scientific Articles

https://github.com/jilljenn/vae

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Variational Factorization Machines for Preference Elicitation in Large-Scale Recommender Systems

https://github.com/jindeok/CA-GF

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Criteria-Aware Graph Filtering: Extremely Fast Yet Accurate Multi-Criteria Recommendation

https://github.com/jindeok/CF-KAN

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CF-KAN: Kolmogorov-Arnold Network-based Collaborative Filtering to Mitigate Catastrophic Forgetting in Recommender Systems

https://github.com/jindeok/turbo-cf

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Turbo-CF: Matrix Decomposition-Free Graph Filtering for Fast Recommendation

https://github.com/jindi-tju/CGMN

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CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning

https://github.com/jinfeng-xu/fkan-gcf

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FourierKAN-GCF: Fourier Kolmogorov-Arnold Network -- An Effective and Efficient Feature Transformation for Graph Collaborative Filtering

https://github.com/jinfeng-xu/nlgcl

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NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation

https://github.com/jinyuz1996/lea-gcn

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Towards Lightweight Cross-domain Sequential Recommendation via External Attention-enhanced Graph Convolution Network

https://github.com/jp-25/end-to-end-adaptive-local-leanring-tall-

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Countering Mainstream Bias via End-to-End Adaptive Local Learning

https://github.com/junewang0614/colarec

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Content-Based Collaborative Generation for Recommender Systems

https://github.com/jyonn/cage

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Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector Quantization

https://github.com/jyonn/cove

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Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector Quantization

https://github.com/kang205/CPRec

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Learning Consumer and Producer Embeddings for User-Generated Content Recommendation

https://github.com/kangliu1225/jmp-gcf

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Joint Multi-grained Popularity-aware Graph Convolution Collaborative Filtering for Recommendation

https://github.com/kevinc2015/simce

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SimCE: Simplifying Cross-Entropy Loss for Collaborative Filtering

https://github.com/kikohs/recog

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Song Recommendation with Non-Negative Matrix Factorization and Graph Total Variation

https://github.com/kilolgupta/Variational-Autoencoders-Collaborative-Filtering

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A Hybrid Variational Autoencoder for Collaborative Filtering

https://github.com/knodis-research-group/neural-cf-for-groups

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Neural Group Recommendation Based on a Probabilistic Semantic Aggregation

https://github.com/knodis-research-group/recklessness-regularization

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Incorporating Recklessness to Collaborative Filtering based Recommender Systems

https://github.com/knodis-research-group/resbemf

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Restricted Bernoulli Matrix Factorization: Balancing the trade-off between prediction accuracy and coverage in classification based collaborative filtering

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