RSPapers/00-Latest RS papers/0306【机器学习与推荐算法】 at 1e1078d1506a996419c1e4bc8cd06287440f3bca · cubean/RSPapers · GitHub

Name		Name	Last commit message	Last commit date
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1-A Survey on Knowledge Graph-Based Recommender Systems-2020.pdf		1-A Survey on Knowledge Graph-Based Recommender Systems-2020.pdf
10-Recommendation on a Budget-Column Space Recovery from Partially Observed Entries with Random or Active Sampling-2020.pdf		10-Recommendation on a Budget-Column Space Recovery from Partially Observed Entries with Random or Active Sampling-2020.pdf
11-AutoEmb-Automated Embedding Dimensionality Search in Streaming Recommendations-2020.pdf		11-AutoEmb-Automated Embedding Dimensionality Search in Streaming Recommendations-2020.pdf
12-A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback-2020.pdf		12-A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback-2020.pdf
13-Syndrome-aware Herb Recommendation with Multi-Graph Convolution Network-2020.pdf		13-Syndrome-aware Herb Recommendation with Multi-Graph Convolution Network-2020.pdf
14-Relation Embedding for Personalised Translation-based POI Recommendation-2020.pdf		14-Relation Embedding for Personalised Translation-based POI Recommendation-2020.pdf
15-Secure Social Recommendation based on Secret Sharing-2020.pdf		15-Secure Social Recommendation based on Secret Sharing-2020.pdf
16-A Collaborative Dual Attentive Autoencoder Method for Recommending Scientific Articles-2020.pdf		16-A Collaborative Dual Attentive Autoencoder Method for Recommending Scientific Articles-2020.pdf
2-Learning to Hash with Graph Neural Networks for Recommender Systems-2020.pdf		2-Learning to Hash with Graph Neural Networks for Recommender Systems-2020.pdf
3-LightGCN-Simplifying and Powering Graph Convolution Network for Recommendation-2020.pdf		3-LightGCN-Simplifying and Powering Graph Convolution Network for Recommendation-2020.pdf
4-Generalized Embedding Machines for Recommender Systems-2020.pdf		4-Generalized Embedding Machines for Recommender Systems-2020.pdf
5-Jointly Learning to Recommend and Advertise-2020.pdf		5-Jointly Learning to Recommend and Advertise-2020.pdf
6-Hybrid Associations Model with Pooling for Sequential Recommendation-2020.pdf		6-Hybrid Associations Model with Pooling for Sequential Recommendation-2020.pdf
7-Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feekback-2020.pdf		7-Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feekback-2020.pdf
8-Using Image Captions and Multitask Learning for Recommending Query Reformulations-2020.pdf		8-Using Image Captions and Multitask Learning for Recommending Query Reformulations-2020.pdf
9-Contextual-Bandit Based Personalized Recommendation with Time-Varying User Interests-2020.pdf		9-Contextual-Bandit Based Personalized Recommendation with Time-Varying User Interests-2020.pdf