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Recommendation for dummy

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추천시스템 개요 및 분류 등.

추천시스템 개요 및 분류 등.

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Transcript

  • 1. A Brief Introduction to ! Recommendation ! (Fallacies & Understanding) Jeong, Buhwan (Ph.D)
  • 2. X Data-driven Automated Personalized
  • 3. Everything, but Nothing
  • 4. For anyone For one in a group For a person For an item
  • 5. Explicit Rating vs Implicit Feedback
  • 6. Content-based Filtering (CBF) Collaborative Filtering (CF)
  • 7. Model-based CF Memory-based CF Matrix Factorization (MF)
  • 8. User-orientation vs Item-orientation I Us Me I Is
  • 9. Similarity Measures ! Many common items between users Many common users between items
  • 10. Similar Items? Similar Users? MxN Co-occurrence, Set theory, Distance, Correlation, Cosine, Kernel
  • 11. Hybrid (Ensemble) Explicit Rating Collaborative Filtering User Orientation Implicit Feedback + Content-based Filtering Item Orientation
  • 12. Search Recommendation Goal Retrieval Discovery Query Keyword User or Item Result Documents Items BM25 CBF PageRank CF Ranking Recency, Quality, Filtering, Diversification
  • 13. ShoppingHow ! Item- & memory-based CF with implicit feedback Hybrid with CBF using category, mall, brand info.
  • 14. Curse of Dimensionality
  • 15. n axa n axN MxN m = Mxa m
  • 16. MF = SVD = LSA/LSI
  • 17. Let’s play music
  • 18. How to Evaluate?
  • 19. Accuracy vs User Satisfaction
  • 20. Fast Iteration >> Good Algorithm
  • 21. Post Analysis & Review
  • 22. New Perspective ! Netflix’s micro tagging/genre Amazon’s anticipatory shipping
  • 23. Cold-start Data sparsity Dimensional complexity Coverage Serendipity & Diversity Explainability
  • 24. PR = P + M + R + F
  • 25. Just do it.