추천시스템 로드맵

• 1.Collaborative Filtering based Recommendation
  • 1.1 Neighborhood-based CF
  • 1.2 Model-based CF
    • 1.2.1 Matrix Factorization
    • 1.2.2 Distance-based CF
      • 1.2.2.1 Euclidean Embedding
      • 1.2.2.2 Metric Learning
• 2.Content-based Recommendation
  • 2.1 Review-based Recommendation
• 3.Knowledge Graph based Recommendation
• 4.Hybrid Recommendation
• 5.Deep Learning based Recommendation
  • 5.1 Multi-layer Perceptron (MLP)
  • 5.2 Autoencoders (AE)
  • 5.3 Convolutional Neural Networks (CNNs)
• 6.Click-Through Rate (CTR) Prediction
• 7.Learning to Rank (LTR)
  • 7.1 Pairwise LTR
  • 7.2 Listwise LTR
• 8.Graph-based Recommendation
  • 8.1 Heterogeneous Information Networks
  • 8.2 Graph Neural Networks
• 9.Social Recommendation
• 10.Cross-domain Recommendation
• 11.Group Recommendation
• 12.Cold Start Recommendation
• 13.Point-of-Interest (POI) Recommendation
• 14.Context-aware Recommendation
• 15.Sequential Recommendation
• 16.Explainable Recommendation
• 17.Conversational Recommendation
• 18.Others
  • 18.1 Evaluation Metrics
  • 18.2 Network Embedding
  • 18.3 Survey on Recommender Systems

 

References

https://github.com/jihoo-kim/awesome-RecSys

GitHub - jihoo-kim/awesome-RecSys: A curated list of awesome Recommender System (Books, Conferences, Researchers, Papers, Github

https://github.com/hegongshan/Recommender-Systems-Paper#metric-learning

GitHub - hegongshan/Recommender-Systems-Paper: Must-read Papers for Recommender Systems (RS)