Why are recommender systems relevant for the economic welfare? This presentation explains the Why based on the economic value for social welfare. Both major types of recommender systems (i.e., Content Filtering and Collaborative Filtering) are explained, its pros and cons. Finally, a hybrid approach of using machine learning and the similiarity of machine learning models is presented and compared to traditional recommender systems.

1. Guide to
Recommender
Systems

2. Who am I
Dr. sc. Amancio Bouza
Intrapreneur, Recommender System Expert
Let’s connect:
https://ch.linkedin.com/in/amanciobouza
Follow me:
https://twitter.com/AmancioBouza

3. 3
1 Mio $
What are

4. 4
1 Mio $Prize for beating Netflix’s
Recommender System by 10%
https://en.wikipedia.org/wiki/Netflix_Prize

5. 5
Why?

6. World has changed: Online Retailer vs B&M Retailer
6
Amazon: 2.3 Mio* B&M: 40k-100k*
[Brynjolfsson et al., 2003]; *number of books

7. for US book market in Year 2000 only… imagine today!
7
$1.03B in
Social Welfare
[Brynjolfsson &. Smith, 2000]

8. 8
What does
it mean?

9. 9
[Brynjolfsson &. Smith, 2000]
Unlimited Online Product Catalogues and Filtering Algorithms have
7-10xHigher Impact on Social Welfare
than Price Competition in Competitive Markets

10. 10
Search Engine:
Consumer searches right Product
Recommender System:
Product finds right Consumer
Paradigm Shift

11. 11
What are
Preferences?

12. 12

13. Preferences can be described with the Utility Function
(Microeconomics)
13

14. 14
Content
Filtering

15. Use Machine Learning to Learn an Individual’s Preferences
15
[Bouza et al., 2009], [Bouza, 2012]

16. 16
-> Good
-> Bad

17. Represent Preferences, e.g., as Decision Tree
17
[Bouza, 2012]

18. Let’s be pragmatic: Machine Learning Model
approximates Utility Function
18
[Bouza, 2012]

19. 19
“Let’s rate an
iPod Nano (green)
on Amazon
and check our
recommendations”,
2008

20. 20
Well…
Do you call this a usefull recommendation?
Based on a personal true story in 2008

21. 21
Collaborative
Filtering

22. Basis for Collaborative Filtering: User-Item Matrix
22

23. 23
Collaborative Filtering:
People who shared similar preferences in the past,
will share similar preferences in the future

24. Collaborative Filtering
24

25. People who share similar prefernces in the past
continue to do so in the future.
25
My favourites:
“BLACKOUT” and “ZERO”
from M. Elsberg

26. People who have similar preferences in the past,
continue to do so in the future.
26
My favourites:
“BLACKOUT” and “ZERO”
from M. Elsberg
What about
“HELIX”?

27. 27
But…

28. 28
What about both Individuals’ Preferences beside Items both rated?
Only Items both rated provides information
?
?
?
?
?
?
? ?

29. 29
Hypothesis-Based
Collaborative
Filtering
[Bouza, 2008-2012]

30. Similarity of Hypothesized Preferences aka Machine Learning Models
30
[Bouza, 2009]

31. 31
Daily Challenges in
Data Preparation
What’s the
movie title
in China?

32. 32
Translated Movie Title:
He’s a
Ghost
http://thoughtcatalog.com/nico-lang/2013/09/51-hilariously-bad-translations-of-movie-titles-that-are-better-than-the-original/

33. 33
Don‘t worry,
no spoiler ;)

34. Hypothesized Partial Preferences (HPP)
34

35. 35

36. Hypothesized Partial Preferences Similarity Matrix
36
[Bouza, 2012]

37. Similarity of two Machine Learning Models
37
[Bouza, 2012]

38. Hypothesis-Based Collaborative Filtering outperforms others
38
[Bouza, 2012]

39. 39

40. 40
Other Applications of
Machine Learning
Model Similarity

41. Applying Hypothesis-Based Collaborative Filtering to
Cross-Project Defect Prediction
41[Zenger & Bouza, 2011] http://www.merlin.uzh.ch/publication/show/4535

42. Building a Texas Hold’em Poker Bot based on a Hypothesis-Based
Collaborative Filtering Approach
42[Kaul & Bouza, 2011] http://www.merlin.uzh.ch/publication/show/2488

43. Group Recommendations in Google Wave Conversion
via Chat Bot and Recommender API
43[Bouza, 2009] http://blog.cpoet.net/providing-movie-recommendations-to-groups-in-google-wave/

44. Your turn!
Where are you going to apply this magic formula?
44

45. Get it
Free Download:
https://www.linkedin.com/pulse/how-ais-collaborate-retrieve-
recommendations-everybody-amancio-bouza?trk=pulse_spock-
articles
Buy Book on Amazon: 110$
https://www.amazon.com/dp/1105585085
45

46. More Recommendation Applications: http://blog.cpoet.net/
Find more application and ideas that master and bachelor students implemented here:
http://blog.cpoet.net/what-makes-a-location-really-interesting-and-how-to-exploit-it-to-improve-location-recommendations/
http://blog.cpoet.net/applying-collaborative-filtering-to-cross-project-defect-prediction/
http://blog.cpoet.net/building-an-agent-for-texas-holdem-poker-based-on-a-recommender-system/
http://blog.cpoet.net/how-ratings-and-trust-inferrencing-establish-healthyow-ratings-and-trust-inferencing-establish-h-online-
markets/
http://blog.cpoet.net/considering-seasonal-effects-in-location-recommendations-with-recommendation-clouds-metaphor/
http://blog.cpoet.net/proposal-using-collaborative-filtering-to-create-a-win-win-win-situation-and-engage-people-with-ubs-
has-been-awarded/
http://blog.cpoet.net/applicability-of-social-network-graph-patterns-to-recommender-systems/
http://blog.cpoet.net/providing-movie-recommendations-to-groups-in-google-wave/
http://blog.cpoet.net/personal-private-movie-recommender-system-at-the-semantic-web-challenge/
http://blog.cpoet.net/202/
http://blog.cpoet.net/personal-cross-site-movie-recommender-system-implemented-as-mozilla-firefox-add-on/
http://blog.cpoet.net/probabilistic-partial-user-model-similarity-for-collaborative-filtering/
http://blog.cpoet.net/semtree-ontology-based-decision-tree-algorithm-for-recommender-systems-at-the-iswc-2008/
46

47. Let’s connect and exchange ideas
Dr. sc. Amancio Bouza
Recommender System Expert, Intrapreneur
Let’s connect:
https://ch.linkedin.com/in/amanciobouza
Follow me:
https://twitter.com/AmancioBouza