Recommender System paper review (+code)
1.Variational Autoencoders for Collaborative Filtering
Variational Autoencoders for Collaborative Filtering 논문 리뷰, 코드 리뷰
2.Embarrassingly Shallow Autoencoders for Sparse Data
Embarrassingly Shallow Autoencoders for Sparse Data 논문 리뷰, 코드 리뷰
3.Deep Variational Autoencoder with Shallow Parallel Path for Top-N Recommendation (VASP)
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Deep Variational Autoencoder with Shallow Parallel Path for Top-N Recommendation (VASP) 논문 리뷰, 코드 리뷰
4.Predicting Consumption Patterns with Repeated and Novel Events
Predicting Consumption Patterns with Repeated and Novel Events 논문 리뷰, 코드 리뷰
5.Gravity-Inspired Graph Autoencoders for Directed Link Prediction
Gravity-Inspired Graph Autoencoders for Directed Link Prediction 논문 리뷰, 코드 리뷰
6.Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders
Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders 논문 리뷰, 코드 리뷰
7.Negative Interactions for Improved Collaborative Filtering: Don’t go Deeper, go Higher
Negative Interactions for Improved Collaborative Filtering: Don’t go Deeper, go Higher 논문 리뷰, 코드 리뷰
8.SiReN: Sign-Aware Recommendation Using Graph Neural Networks
SiReN: Sign-Aware Recommendation Using Graph Neural Networks 논문리뷰, 코드리뷰
9.Negative Can Be Positive: Signed Graph Neural Networks for Recommendation
Negative Can Be Positive: Signed Graph Neural Networks for Recommendation 논문리뷰, 코드리뷰
10.PANE-GNN: Unifying Positive and Negative Edges in Graph Neural Networks for Recommendation
PANE-GNN: Unifying Positive and Negative Edges in Graph Neural Networks for Recommendation 논문리뷰, 코드리뷰
11.LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation 논문리뷰, 코드리뷰