This slides describes conceptual algorithms of using content-based and Collaborative filtering to model beer recommender.

1. Content-based and Collaborative
Filtering Beer Recommender
Hsiang-Hsuan Hung (HHH)
Hsiang.Hung2015@gmail.com

2. I like Budweiser!!!
, , ….
> 3000 products
Enhance customers’ satisfaction, so more purchase
Why Do We Need Recommendation?

3. • Always give good recommendation to customers
• Understand customers’ demographic characteristics
Why Do We Need Recommendation?

4. I rate for !
but for !
Rating = ? ?

5. I rate for !
but for !
pred = 4.3
pred = 1.7
recommend

6. Personalized Recommendation
5
Different people have different tastes, so rating.
14.5 3
1.5 1 3.5 5

7. • Supervised learning, regression problem.
• Central concept: Similarity
a.) item-item recommendation (Amazon)
b.) user-item recommendation
Content-based filtering (Pandora)
Collaborative filtering (Netflix, Spotify)
Personalized Recommendation

8. Item-Item recommendation

9. When Simpsons (id=7) is browsing #144 beer:
Recommendation Engine User Interface

10. When Simpsons (id=7) is browsing #144 beer:
Recommendation Engine User Interface
item-item reco

12. Data Source: BeHoppy (SQL)

13. The Beer Content Data

14. The Beer Content Data
Beer id
Beer vectors are represented by features:

15. Beer Vector Space

16. Cosine Similarity
is more similar
to than

17. Cosine Similarity
is more similar
to than

18. When Simpsons (id=7) is browsing the beer page:
Recommendation Engine User Interface
user-item reco

19. User-Item recommendation

20. customers’ ratings:
Simpsons
Content-based reco systems, Pazzani1and and Billsus
Content-Based Filtering

21. customers’ ratings:
Simpsons HHH
Content-based reco systems, Pazzani1and and Billsus
Content-Based Filtering

22. Each user is a regression problem:
Content-Based Filtering

23. Predict
Each user is a regression problem:
Content-Based Filtering

24. Cold Start: Spare Rating Data
D: For most users, little data points, low accuracy:
number of users: 1728
number of beers: 1854
number of ratings: 12360
sparsity = 0.9962%

25. 1 2 5 1 2 2
5 6 7 8 9 10 11
1
3 5 2 5 5 4
5 4 1 5 5 4
Collaborative Filtering (CF)
rating table
users
4
4 4
beers
• No content features are needed.
• Consider user-item interactions
7
33
34
42
45
47
48

26. Collaborative Filtering (CF)
• No content features are needed.
• Consider user-item interactions
• Neighborhood model
– item based (use item similarity)
– user based (use user similarity)
• Latent-factor-model

27. users
beers
Vector Space by Beer Rating Vectors
7
33
34
42
45
47
48
..7 8 9 10 11 ..
beer similarity:

28. Sarwar, Karypis, Konstan, and Riedl , (2001)
Neighborhood Model – Item Based

29. Sarwar, Karypis, Konstan, and Riedl , (2001)
Neighborhood Model – Item Based
k-nearest-neighbor:

30. Sarwar, Karypis, Konstan, and Riedl , (2001)
Neighborhood Model – Item Based
k-nearest-neighbor:
K=3

31. Sarwar, Karypis, Konstan, and Riedl , (2001)
similarity as
weight
Neighborhood Model – Item Based
Model = kNN + weight:

32. users
beers
7
33
34
42
45
5 6 7 8 9 10 11 12
user similarity:
Vector Space by User Rating Vectors

33. Herlocker, Konstan, Borches and Riedl , (1999)
weight
Neighborhood Model – User Based
Find similar users:

34. Metrics
mean absolute error:
beer content and ratings as features
Sarwar, Karypis, Konstan, and Riedl , (2001)
a) item-based:
b) user-based:
c) Baseline:

35. Model Performance
k=10-20

36. Revised Neighborhood - Adjusted Ratings

37. • NOT scalable.
• Not easy to incorporate additional information, e.g.
purchase, browse history (implicit data).
• Alternative: latent-factor model!
Disadvantages of the Model

38. Vector Spaces

39. Customer-Beer Vector Space
f: # of latent factors

40. Matrix-Factorization for Latent-Factor Model
rating matrix
user preference
beer features
()
()
musers
n beers
n
m
f
f
Computer (2009), Koren, Bell and Volinsky
1 2 5 1 2 2
3 5 2 5 5 4
5 4 1 5 5 4
4
4 4
()
7
33
34
42
45
47
48
5 6 7 8 9 10 11

41. •SVD, not scalable
•Gradient descent, not convex problem
•Alternating least square (ALS)!
Matrix-Factorization Linear Regression
user-item interaction
bias
regularization

42. •At each step, fix one variable, and solve minimization:
fix , solve fix , solve fix , solve
What is the ALS?
()
()
()
Rating
matrix

43. 5 6 7 8 9 10 11
More Detail: Normal Equations
7
33
34
42
45
47
48
Hu, Koren and Volinsky, 2008

44. 5 6 7 8 9 10 11
7
33
34
42
45
47
48
Hu, Koren and Volinsky, 2008
More Detail: Normal Equations

45. ALS Model Performance (on Spark)

46. Implicit Data: e-Commerce Data
Implicit feedback
(data warehouse)
Explicit feedback
(beHoppy)
purchase frequency
ratings
Hu, Koren and Volinsky, 2008

47. • Logistic regression + confidence weight
CF Using Implicit Data
user-item interaction
bias
regularization
confidence
Hu, Koren and Volinsky, 2008
preference

48. Implicit Data CF Performance
•Metric: percentile-ranking
•Random:
•CF:
Baseline:

49. Recommender Pipeline
e-commerce datarating data
web
server
browse
reco
Hybrid recommender