This document discusses recommendation systems and the Bayesian Personalized Ranking (BPR) framework. It introduces the goal of recommendation systems to increase product sales through relevance, novelty, serendipity and diversity. It also discusses different recommendation approaches, including collaborative filtering, content-based filtering and knowledge-based filtering. A key part of the document is describing the BPR framework, which uses a Bayesian approach to learn a personalized ranking model from implicit feedback data. It formalizes the recommendation problem as optimizing a posterior distribution over the preferences of users through matrix factorization.

1. Steffen Rendle et al.
Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
(UAI2009)

2. UNIST Financial Engineering Lab. 1

3. UNIST Financial Engineering Lab. 2

4. UNIST Financial Engineering Lab. 3

5. UNIST Financial Engineering Lab. 4

6. UNIST Financial Engineering Lab. 5
Goal - Increasing Product Sales
Relevance
Novelty
Serendipity
Diversity
Problem Formulation
Matrix Completion Problem
Top-k recommendation Problem

7. UNIST Financial Engineering Lab. 6
Collaborative Filtering
User-Item interaction 정보 활용 (평점, 좋아요, 장바구니, 구매내역 등…)
Content-Based
Attribute information 활용 (유저 프로필, 상품 정보 등)
Knowledge-Based
Domain Knowledge 또는 Constraint가 가미된
Demographic
Hybrid
Context-Based
Time-Sensitivity

8. UNIST Financial Engineering Lab. 7
Collaborative Filtering
User-Item interaction 정보 활용 (평점, 좋아요, 장바구니, 구매내역 등…)
Content-Based
Attribute information 활용 (유저 프로필, 상품 정보 등)
Knowledge-Based
Domain Knowledge 또는 Constraint가 가미된
Demographic
Hybrid
Context-Based
Time-Sensitivity

9. UNIST Financial Engineering Lab. 8

10. UNIST Financial Engineering Lab. 9

11. UNIST Financial Engineering Lab. 10

12. UNIST Financial Engineering Lab. 11

13. UNIST Financial Engineering Lab. 12

14. UNIST Financial Engineering Lab. 13

15. UNIST Financial Engineering Lab. 14

16. UNIST Financial Engineering Lab. 15

17. UNIST Financial Engineering Lab. 16
유튜브에서 같은 영상을 틀었을 때 옆의 추천 영상 비교

18. UNIST Financial Engineering Lab. 17
위의 4.
3.
2.

19. UNIST Financial Engineering Lab. 18
https://leehyejin91.github.io/post-bpr/
옆의 그림에서 +를 모아 놓은 set

20. UNIST Financial Engineering Lab. 19

21. UNIST Financial Engineering Lab. 20
중간 정리
Binary Relation 을 정의
유저 u가 좋아요 누른 아이템의 set
아이템 i에 좋아요 누른 유저의 set
위의 4.
3.
2.

22. UNIST Financial Engineering Lab. 21
Posterior
Likelihood
Prior
(MF에 적용시)

23. UNIST Financial Engineering Lab. 22
(A) 모든 유저는 독립
𝑢, 𝑖, 𝑗 ∈ 𝐷𝑠 ↔ 𝑖 >𝑢 j

24. UNIST Financial Engineering Lab. 23
(MF에 적용시)
ෝ
𝒙𝒖𝒊 ෝ
𝒙𝒖𝒋 𝒑(𝒊 >𝒖 𝒋)
1 0 𝜎(1)
0 1 𝜎(−1)
0 0 𝜎(0)
1 1 𝜎(0)

25. UNIST Financial Engineering Lab. 24

26. UNIST Financial Engineering Lab. 25
Posterior
Likelihood
Prior
(MF에 적용시)

27. UNIST Financial Engineering Lab. 26
Full Gradient Descent vs Stochastic Gradient Descent

28. UNIST Financial Engineering Lab. 27
ෝ
𝒙𝒖𝒊

29. UNIST Financial Engineering Lab. 28

30. UNIST Financial Engineering Lab. 29

31. UNIST Financial Engineering Lab. 30
𝑎𝑟𝑔𝑚𝑎𝑥
𝜃

32. UNIST Financial Engineering Lab. 31
𝑆 = 𝑢1, 𝑖2 , 𝑢1, 𝑖3 , 𝑢2, 𝑖1 , 𝑢2, 𝑖4 …

33. UNIST Financial Engineering Lab. 32
𝑆 = 𝑢1, 𝑖2 , 𝑢1, 𝑖3 , 𝑢2, 𝑖1 , 𝑢2, 𝑖4 …
𝐼𝑢1
+
= 𝑖2, 𝑖3
𝑆𝑡𝑟𝑎𝑖𝑛 = 𝑢2, 𝑖1 , 𝑢2, 𝑖4 …
𝑆𝑡𝑒𝑠𝑡 = 𝑢1, 𝑖2 , 𝑢1, 𝑖3
Evaluation Pairs
For user 1,

34. UNIST Financial Engineering Lab. 33
For user 2, and so on …

35. UNIST Financial Engineering Lab. 34

36. UNIST Financial Engineering Lab. 35
Thank you for listening!