Beer Recommender Systems Hsiang-Hsuan Hung (HHH) [email protected] h=ps://github.com/HsiangHung/BI-analysis/Recommender h=p://www.hsianghung.tech
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Beer Recommender Systems
Hsiang-HsuanHung(HHH)[email protected]
h=ps://github.com/HsiangHung/BI-analysis/Recommender
h=p://www.hsianghung.tech
IlikeBudweiser!!!
5 13
Beer RecommendaEon
?
IlikeBudweiser!!!
5 13
1 3.5 5Idon’tlikeBudweiser
Beer RecommendaEon
?
?
• Supervised learning, regression problems.
• Central concept: Similarity
1.) item-itemrecommendaEon (Amazon)
2.) user-itemrecommendaEon
CollaboraEve filtering (NeOlix, SpoEfy)
Personalized RecommendaEon
Recommender Pipeline
e-commerce data raEng data
web server
browse
reco
Hybrid recommender
RecommendaEon Engine Dashboard
item-item reco user-item reco
When Mr. Simpsons (id=7) is browsing beer-144:
RecommendaEon Engine Dashboard
item-item reco user-item reco
When Mr. Simpsons (id=7) is browsing beer-144:
user
s
beers
5 4 1 5 5 4
5 5 5 34 1 1
11 5 1 221
33
CollaboraEve Filtering (CF)
4 44
34 1
b8 = (1,�, 5,�, 1,�, 4)
b9 = (5,�, 1,�, 5, 4,�)
b10 = (5, 1, 2,�, 5, 4, 1)
17 33 34
42
45
47
48
5 6 7 8 9 10 11 12
Beer Vectors: raEng table
DB:BeHoppy
Beer Vector Space and Cosine Similarity
b1
b2
b4
b3
b 2 Rnum of users
b1
b2
b4
b3
sA,B =bA · bB
|bA||bB |
is more similar
to than
Beer Vector Space and Cosine Similarity
b 2 Rnum of users
When Mr. Simpsons (id=7) is browsing beer-144:
user-item reco
RecommendaEon Engine Dashboard
item-item reco
ru,3 = 4.5
ru,4 = 1
ru,2 = 3
b 2 Rm
ru,5 = 4
R̂u,1 =?
Neighborhood Models
rH,x = 1
rM,x
= 4.5
rT,x
= 5
R̂S,x
=?
u 2 Rn
ModelB: user-based ModelA: item-based
Model C: Latent-Factor Model (Easily Scale Up)
users preferences beer features
( )
( ) m u
sers
n beers n
m
f
f ⇡ ⇥
Computer(2009),Koren,BellandVolinsky
1 2 5 1 2 2
3 5 2 5 5 4
5 4 1 5 5 4
4 4 4
(�uS�)
(�uH�)
(�uK�)...
( )
b1 b2 b3
7 33 34 42 45 47 48
567891011
ru,i ' uTubipredicEon
User-Beer Vector Space
uS
u,b 2 Rf
R̂S,1 = uTSb1
f: # of latent factors b1
Models Performance
MAE =1
N
X
u,i
|R̂u,i � ru,i|
k=10-20
Challenges
(implicit purchase frequency) ru,b = 1� 5
Hu,KorenandVolinsky,2008 ru,b 2 I
• Cold Start (need more raEngs).
• Integrate implicit data: e-commerce data.
• Define confidence for each customer.
(explicit raEng)
My Background
• Physics PhD@UCSD (2011)
• ComputaEonal Physicist@UT AusEn and UIUC (2012-2015)
• ComputaEonal physics and materials science
• Data Engineering Fellow@Insight (2016)
• Data ScienEst/Engineer@Anheuser-Busch (2016)
Thank you!
Sarwar,Karypis,Konstan,andRiedl,(2001)
weight
R̂u,i =
Pj2Sk sijru,jPj2Sk |sij |
ru,3 = 4.5
ru,4 = 1
ru,2 = 3
kNN + weight:
b 2 Rm
ru,5 = 4
=(0.8 ⇤ 4.5 + 0.7 ⇤ 4 + 0.2 ⇤ 3)
0.8 + 0.7 + 0.2
R̂u,1 =?
Model A: Item-based Neighborhood
users
beers
5 4 1 5 5 4
5 5 5 34 1 1
11 5 1 221
33
RaEngs as Features of Users Vectors
4 44
34 1
u7 = (5, 4,�, 1, 5, 5, 4,�)
u34 = (1, 1,�, 5, 1, 2, 2,�)
u45 = (4, 5,�, 1, 5, 5, 3, 1)
s45,7 > s45,34
7 33 34
42
45
47
48
5 6 7 8 9 10 11 12
Herlocker,Konstan,BorchesandRiedl,(1999)
weight
rH,x = 1
rM,x
= 4.5
rT,x
= 5R̂
u,x
=
Pv2S
k suv
rv,xP
v2S
k |suv
|
R̂S,x
=?
Find similar users:
u 2 Rn
Model B: User-based Neighborhood
What is the ALS?
( )
( )
(�uS�)
(�uH�)
(�uK�)...
( )
b1 b2 b3RaEng matrix ⇡ ⇥ R = UBT· · ·
567891011uS =
0
BBB@
uS,1
uS,2...
uS,f
1
CCCA=
⇣BTB+ �I
⌘�1BT
0
BBB@
rS,1rS,2...
rS,n
1
CCCA
More Detail: Normal EquaEons
rS
7 33 34 42 45 47 48
Hu,KorenandVolinsky,2008
567891011
bi =
0
BBB@
bi,1bi,2...
bi,f
1
CCCA=
⇣UTU+ �I
⌘�1UT
0
BBB@
r1,ir2,i...
rm,i
1
CCCA
uS =
0
BBB@
uS,1
uS,2...
uS,f
1
CCCA=
⇣BTB+ �I
⌘�1BT
0
BBB@
rS,1rS,2...
rS,n
1
CCCA
= ri
rSrS,i
7 33 34 42 45 47 48
Hu,KorenandVolinsky,2008 More Detail: Normal EquaEons
ImplemenEng ALS in Spark
• AlternaEng least square (ALS)
Solving Matrix-FactorizaEon LR
regularizaEon
minu,b,⇠
X
(u,i) if ru,i 6=0
⇣ru,i � uT
ubi � ⇠u,i⌘2
+ �⇣X
u
|uu|2 +X
i
|bi|2⌘
• ALS: at each step, fix one variable, and solve minimizaEon: fix , solve fix , solve fix , solve u ub b u b
⇠
Grid Search Using Cross-ValidaEon
�⇣|u|2 + |b|2
⌘
• LogisEc regression + confidence weight
CF Using Implicit Data
minu,b,⇠
X
(u,i)
cu,i⇣pu,i � uT
ubi � ⇠u,i⌘2
+ �⇣X
u
|uu|2 +X
i
|bi|2⌘
user-item interacEon
bias
regularizaEon
confidence Hu,KorenandVolinsky,2008
cu,i = 1 + ↵ru,i
cu,i = 1 + ↵ log (1 + ru,i/✏)
cu,i = 1 + ↵ log (1 + ru,i/✏) + �ru,i
pu,i = 0/1
Implicit Data CF Performance
• Metric: percenEle-ranking
rank =
Pu,i ru,i ⇤ ranku,iP
u,i ru,i
• Random: rank = 50%
• CF (f=20):
Baseline: rank ⇠ 29%
rank ⇠ 16%
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