14. DRINK RECOMMENDER
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Story:
After spending 8 years studying engineering in college, I decided to become a bartender.
I kept notes of what my customers liked and disliked.
I want to create a recommender system to suggest and create new drinks that my
customer will most likely buy.
15. GETTING TO KNOW MY MAKE-BELIEVE
CUSTOMER
ANALYZING DATA
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User
info
itemsUser
16. Miranda:
Likes more dessert-y type drinks. Not into bitter drinks. Can’t tell if Miranda is rich.
Sansa:
She likes Martini and hates everything else? Don’t know much about her.
Cersie:
Only into strong coffee drinks. She likes alcoholic drinks and she’s rich.
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17. I CAN SPEND HOURS, DAYS, MONTHS, YEARS AND PERHAPS DECADES
ANALYZING THE DATA MANUALLY.
I NEED TO FIND A WAY TO FACTOR INFORMATION ABOUT MY
CUSTOMERS, DRINKS AND INTERACTION BETWEEN THEM TO
AUTOMATE THE RECOMMENDING PROCESS USING A COMPUTER.
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INFORMATION OVERLOAD
I should’ve taken that
machine learning course...
18. PERSONALIZATION
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Connecting user to items
User | Movies
User | Products
User | Music
Is browsing every movie, product, or music even
practical?
“Need new ways to discover content”.
19. PERSONALIZATION
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● User to Drinks?
● User to User?
● User to medicine?
Connecting user to items
User | Movies
User | Products
User | Music
20. PERSONALIZING DRINKS
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Bartender's note:
Enio ordered a Martini last weekend in the evening.
He ordered a coffee on Monday at noon.
A good recommender system adapt with time and is capable of
considering multiple sessions.
21. POPULAR RECOMMENDER (LEVEL 0)
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Count:
Item:
Popular recommender, recommends the
most popular item on the menu, Espresso.
● Completely lacks personalization. My
customers have different taste and they
want drinks that match their interests.
22. CLASSIFICATION MODEL (LEVEL 1)
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User info
Item info
Past history
Others
Classification
model
Yes, Miranda will probably like Mocha.
No, Miranda will probably dislike Mocha.
Pros : Personalized. Capture context.
Cons: We don’t have access to all these info and if we input wrong information, we
will make wrong predictions.
23. COLLABORATIVE FILTERING (LEVEL 2)
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People who liked X also liked Y.
If Ramin likes X, he might also like … Y.
Co-occurrence Matrix (A symmetrix item-item matrix)
Item Espresso Martini Mocha
Espresso a 3 2
Martini 3 b 7
Mocha 2 7 c
Number of people
purchase both Mocha
and Espresso
Jose bought Mocha the other day, what should I recommend him to now?
Martini
24. COLLABORATIVE FILTERING (LEVEL 2)
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Item Espresso Martini Mocha Coffee
Espresso a 3 2 45
Martini 3 b 7 23
Mocha 2 7 c 39
Coffee 45 23 39 d
Popular Item Effect
No matter what Jose has purchased, the recommender system will recommend coffee.
There are ways to normalize the data to avoid the popular item effect.
25. COLLABORATIVE FILTERING (LEVEL 2)
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Item Espresso Martini Mocha Coffee
Espresso a 3 2 45
Martini 3 b 7 23
Mocha 2 7 c 39
Coffee 45 23 39 d
No History
I’m only looking at Jose most current purchase of Mocha. What if he bought a Martini before
and he didn’t like it?
26. COLLABORATIVE FILTERING (LEVEL 2)
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Item Espresso Martini Mocha Coffee
Espresso a/N 3/N 2/N 45/N
Martini 3/N b/N 7/N 23/N
Mocha 2/N 7/N c/N 39/N
Coffee 45/N 23/N 39/N d/N
Weighted Average of purchased items
I want to know if I should recommend coffee to Jose.
Score_coffee = ⅓(S_coffee,mocha + S_coffee,martini + S_coffee,espresso)
Sort the scores and pick the one with highest score.
N: Normalizing factor
Purchase History
Jose:
Mocha - YES
Martini - NO
27. COLLABORATIVE FILTERING (LEVEL 2)
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More problems!!!
● Can’t utilize context like time of the day.
● Can’t utilize my customer age to my advantage.
● Can’t utilize information about my drinks and
their ingredients to make better recommendation.
And WHAT IF:
I have a new customer, what should I recommend?
What if I’m making a new drink, who would buy it?
A Cold Start Problem.
29. MATRIX FACTORIZATION (LEVEL 3)
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● We need a recommender system that factors more than just users past purchase history.
● A system that can factor more personalized info about the user and the product and the time
of the visit into account as well plus all the goodies we get from the collaborative filtering.
To learn from data even when not available. (Very sparse matrices, missing data.)
Keep in mind that each user only tries a
few drinks.
Rating =
Users
ItemsRating available from
user U for item V
Rating unavailable
We don’t know what the
user thinks about item V
30. MATRIX FACTORIZATION (LEVEL 3)
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Rating =
Users
Items
We need to fill in the white boxes using
ALL of the available info we got.
Dictionary
(Bases)
Activation Matrix
(Encodings)
x
~
A whole lot of fancy math
happens here to factorize the
rating matrix as multiplication
of two other smaller matrices
that uncover those hidden
area by minimizing some cost
function.
31. BLENDING MODELS (LEVEL 4)
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Point : There is no universal recommender systems that work for everything.
We need to blend different models to be able to attack different applications.
Netflix Challenge
From 100 million movie ratings
rate 3 millions of them to highest
accuracy.
Winning team blended over
100 models to gain 10.35%
improvement in the accuracy.
(and got 1 million dollars for it!)
32. PERFORMANCE METRIC
RECOMMENDER SYSTEMS
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RMS
● Fraction of items correctly recommended.
BUT:
● We care about what the user liked more than what they don’t.
● Imbalanced information can skew the results.
● With this metric you can get good accuracies by recommending nothing at all!
Recall
(# liked & # shown) / # liked
● How many of the items that the user liked, was actually recommended?
The world we’re looking at only contains the liked items.
Precision
(# liked & # shown) / # shown
● Out of the recommended items, how many items did the user actually
liked? The world is all the recommended items. How much garbage should
I look at until I found what I like (attention span).
33. OPTIMAL RECOMMENDER
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Recall
(# liked & # shown) / # liked
Maximize Recall => Recommend everything!
Recall = 1 But, Precision = very small
Precision
(# liked & # shown) / # shown
Best Recommender would
recommend only the products the user like.
Precision = 1
Recall = 1
Point: Use both precision and recall among other metrics RMS to
evaluate your recommender system.
34. PRECISION - RECALL CURVE
VARY #ITEMS
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Optimal Recommender:
● Precision stays at 1, because it’s
only recommending what I like.
● Recall increases, because it’s
uncovering more of the items that
the user like as we are increasing
the threshold on the number of
items.
One liked item
recommended.
1 2 3 4 …./ total number of interests
Recall
Precision
All liked item are
recommended.
35. PRECISION - RECALL CURVE
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A more realistic recommender:
As we recommend more items to the user,
the area below the precision-recall curve
drops, we start introducing garbage!
1 2 3 4 …./ total number of interests
Recall
Precision
Realistic
recommender
(smoothed out)
36. PRECISION - RECALL CURVE
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A more realistic recommender:
As we recommend more items to the user,
the area below the precision-recall curve
drops, we start introducing garbage!
Based on the application, you could also
look at some weighted average of these
metrics as well. For now, we can look at the
area below the curve to choose which
recommender system to choose.
1 2 3 4 …./ total number of interests
Recall
Precision
Orange curve is a better
recommender system
than the red one.
37. DRINK RECOMMENDER - AN EVALUATION
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Matrix Factorization and
Similarity based
Recommender (with
item info+ with/out user
info)
Similarity based
recommender
Matrix factorization
recommender
39. BACK TO OUR AARP SERVICE RECOMMENDER
SYSTEM
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Data and Training:
● 100 participants.
● 46 questions
● Imbalanced demographic classes
● Missing data less than 1%
● Training with 80% of the data
● Tuning with cross validation
● Testing with 20% of the data
User info -
Salary User info -
Age
Item info-
Outdoors
