추천시스템 개요 및 분류 등.

1. A Brief Introduction to
!
Recommendation
!
(Fallacies & Understanding)
Jeong, Buhwan (Ph.D)

4. X
Data-driven
Automated
Personalized

5. Everything, but Nothing

6. For anyone
For one in a group
For a person
For an item

7. Explicit Rating vs Implicit Feedback

8. Content-based Filtering (CBF)
Collaborative Filtering (CF)

9. Model-based CF
Memory-based CF
Matrix Factorization (MF)

10. User-orientation vs Item-orientation
I
Us
Me
I
Is

11. Similarity Measures
!
Many common items between users
Many common users between items

12. Similar Items?
Similar Users?
MxN
Co-occurrence, Set theory, Distance, Correlation, Cosine, Kernel

13. Hybrid (Ensemble)
Explicit Rating
Collaborative Filtering
User Orientation
Implicit Feedback
+
Content-based Filtering
Item Orientation

14. Search
Recommendation
Goal
Retrieval
Discovery
Query
Keyword
User or Item
Result
Documents
Items
BM25
CBF
PageRank
CF
Ranking
Recency, Quality, Filtering, Diversification

15. ShoppingHow
!
Item- & memory-based CF with implicit feedback
Hybrid with CBF using category, mall, brand info.

16. Curse of Dimensionality

17. n
axa
n
axN
MxN
m
=
Mxa
m

18. MF = SVD = LSA/LSI

19. Let’s play music

20. How to Evaluate?

21. Accuracy vs User Satisfaction

22. Fast Iteration >> Good Algorithm

23. Post Analysis & Review

24. New Perspective
!
Netflix’s micro tagging/genre
Amazon’s anticipatory shipping

25. Cold-start
Data sparsity
Dimensional complexity
Coverage
Serendipity & Diversity
Explainability

26. PR = P + M + R + F

27. Just do it.