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The quest for prediction accuracy
FrosmoX17
Mekbib Awono 17.10.2017
If I have 2 million customers on the web,
I should have 2 million stores on the web.
- Jeff Bezos, CEO of Amazon.com™ -
Agenda
3
• Basic recommendation models
• Going beyond basic models
• Combined model to capture complexities
• Conclusion
Basic recommendation models
4
• User-based: recommendations based on users with similar interests
• Item-based: recommendations based on items with similar attributes
Going beyond basic models
• More complex models
• Product bundles
• Gift items/Special interest items vs long enduring interest
5
More time complexity
6
• Time directionality
• Items bought sequentially
• E.g. new parents buy:
baby bottles and teethers balance bikes, board books
• Not reasonable to recommend against the arrow of time directionality
Do you feel like watching the same type of movies
early in the morning as late in the evening?
Temporal dynamics
8
• People who agree on early morning movies do not necessarily agree on late
evening movies
• One of the major achievements in the Netflix prize competition
Do users in gambling services behave the same on
Friday evening and on Monday morning?
Combined model to capture complexities
10
• User-Item matrix
Item-1 Item-2 Item-3
User-1 5 4 ?
User-2 ? 3 2
User-3 ? ? 1
User-4 2 ? ?
Combined model to capture complexities
11
• User-Item matrix
Item-1 Item-2 Item-3
User-1 5 4 ?
User-2 ? 3 2
User-3 ? ? 1
u1
u2
u3
u4
…. un
v1
v2
v3
v4
... vn
User-1 : User vector : Ui
Item-1: Item vector : Vj
u1
u2
u3
u4
… un
Ui
v1
v2
v3
..
vn
VT
j
= ̂ri, j
Estimated rating calculation
12
= overall mean rating,
bi
u
= mean rating of user i
bi
I
= mean rating of item i
ui
= user feature vector
vj
T
= movie feature vector (transpose)
12
u1
u2
u3
u4
…. un
v1
v2
v3
v4
... vn
User-1 : User vector : Ui
Item-1: Item vector : Vj
12
u1
u2
u3
u4
… un
Ui
v1
v2
v3
..
vn
VT
j
= ̂ri, j
Learning the data source to minimize errors
13
To learn the factor vectors, minimization is done using SGD or ALS learning methods
Training stage Testing stage
User-item matrix
Training data Test data
Deployment
Upper hand of Matrix factorization
14
• Matching users with no common rated items but with underlying common
preference
• Matching items with different descriptions
• Being able to express abstract interests
Conclusion
15
• Basic models do not accurately capture the real-world scenario
• Accurate predictions require complex models
• Merge user-based and content-based models
• More accurate prediction as a result
Thank You
www.frosmo.com
Mekbib Awono
Developer
mekbib.awono@frosmo.com

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Quest for prediction accuracy by Mekbib Awono

  • 1. The quest for prediction accuracy FrosmoX17 Mekbib Awono 17.10.2017
  • 2. If I have 2 million customers on the web, I should have 2 million stores on the web. - Jeff Bezos, CEO of Amazon.com™ -
  • 3. Agenda 3 • Basic recommendation models • Going beyond basic models • Combined model to capture complexities • Conclusion
  • 4. Basic recommendation models 4 • User-based: recommendations based on users with similar interests • Item-based: recommendations based on items with similar attributes
  • 5. Going beyond basic models • More complex models • Product bundles • Gift items/Special interest items vs long enduring interest 5
  • 6. More time complexity 6 • Time directionality • Items bought sequentially • E.g. new parents buy: baby bottles and teethers balance bikes, board books • Not reasonable to recommend against the arrow of time directionality
  • 7. Do you feel like watching the same type of movies early in the morning as late in the evening?
  • 8. Temporal dynamics 8 • People who agree on early morning movies do not necessarily agree on late evening movies • One of the major achievements in the Netflix prize competition
  • 9. Do users in gambling services behave the same on Friday evening and on Monday morning?
  • 10. Combined model to capture complexities 10 • User-Item matrix Item-1 Item-2 Item-3 User-1 5 4 ? User-2 ? 3 2 User-3 ? ? 1 User-4 2 ? ?
  • 11. Combined model to capture complexities 11 • User-Item matrix Item-1 Item-2 Item-3 User-1 5 4 ? User-2 ? 3 2 User-3 ? ? 1 u1 u2 u3 u4 …. un v1 v2 v3 v4 ... vn User-1 : User vector : Ui Item-1: Item vector : Vj u1 u2 u3 u4 … un Ui v1 v2 v3 .. vn VT j = ̂ri, j
  • 12. Estimated rating calculation 12 = overall mean rating, bi u = mean rating of user i bi I = mean rating of item i ui = user feature vector vj T = movie feature vector (transpose) 12 u1 u2 u3 u4 …. un v1 v2 v3 v4 ... vn User-1 : User vector : Ui Item-1: Item vector : Vj 12 u1 u2 u3 u4 … un Ui v1 v2 v3 .. vn VT j = ̂ri, j
  • 13. Learning the data source to minimize errors 13 To learn the factor vectors, minimization is done using SGD or ALS learning methods Training stage Testing stage User-item matrix Training data Test data Deployment
  • 14. Upper hand of Matrix factorization 14 • Matching users with no common rated items but with underlying common preference • Matching items with different descriptions • Being able to express abstract interests
  • 15. Conclusion 15 • Basic models do not accurately capture the real-world scenario • Accurate predictions require complex models • Merge user-based and content-based models • More accurate prediction as a result