0926

1. True Fit Skin Care
Chang Liu
Fellow at Insight Data Science 2014

2. So
many
products…
So
many
reviews…
Time
spent
Money
wasted
Happiness
What
makes
it
so
hard?
Overwhelming
informa<on
Reviews
can
be
so
long…
So
many
ingredients…
TRIETHOXYCAPRYLYLSILANE

3. 115k
Reviewers
• 32k
Reviewers
• w/
2+
reviews
1177
Products
• 77
brands
•
8
categories
184k
Reviews
• Ra<ng
[1-­‐5]
• Review
text
• Quick
take
Data:
User
Reviews
Scraped
from
Sephora.com
Reviewers
Product
X
Y
…
1
…
2
…
3
…
… …
… …
N
…
Algorithm:
• Item-­‐centric
collabora<ve
filter
• Pearson’s
correla<on
coefficients
weighted
ra<ng
Cross
Valida;on
• 5-­‐fold
for
reviewers
• Leave-­‐one-­‐out
for
products
• Accuracy
=
86.3%
±
1%

4. Visualize
the
similarity
matrix
On
both
axes:
1177
products
sorted
by
77
brands
White
=
high
similarity
Black
=
low
similarity

5. On
both
axes:
1177
products
sorted
by
77
brands
White
=
high
similarity
Black
=
low
similarity
White
in
a
square
=
User
has
similar
reviews
for
different
products
from
a
brand
=
Strong
customer
loyalty
There
are
structures!

6. Expensive!
“Organic
&
Natural”
High
Cost-­‐Performance
There
are
structures!
For
example…

7. • For
Ac;onable
Insights
customer
Data
tells
you
your
true
fit!
• For
brands
Uniqueness
helps
enhance
customer
loyalty!
• For
Sephora.com
Send
marke<ng
emails
to
new
customers
of
brands
with
stronger
customer
loyalty!

8. My
name
is
Chang
Liu

9. Is
the
ra<ng
a
good
measure
of
reviewers’
perspec<ve?
• Trained
a
NaïveBaysian
classifier
for
sen<ment
analysis
• W/
250
thousand
reviews
from
Birchbox.com
• A
website
that
sends
out
free
samples
from
smaller
brands
and
gathers
massive
user
reviews
Most
common
words
Most
informa;ve
feature
Word
Count
Nega;ve
Posi;ve
skin
91349
re-­‐wash
Penny
product
82481
garbage
hook
use
64044
mediocre
gorgeous
love
55691
ketchup
perk
feel
47879
trash
stock
face
42615
unimpressive
glowing
like
41427
survey
splurge
great
34155
ineffec<ve
effortless
really
31672
gag
Christmas
smell
27621
worthless
happily
text
quick
take
Precision
95.3%
85.4%
Recall
89.8%
93.1%
Worth
every
penny!
Another
Valida;on

10. Another
Valida;on
Is
the
ra<ng
a
good
measure
of
reviewers’
perspec<ve?

11. Product
X
Algorithm:
Item-­‐centric
collabora<ve
filter
similarity
87.4%
Product
Y
Product
X
Product
Y
1
1
1
1
1
1
1
1
1
1
1
1
nΣ
nΣ
nΣ
5-­‐fold
+
leave-­‐one-­‐out
CV:
accuracy
=
86.3%
±
1%
Reviewers
Product
X
Y
…
1
…
2
…
3
…
… …
… …
N
…
Pairwise similarities are measured
by Pearson's correlation coefficients:
cXY =
(Xi − X)(Yi −Y )
i=1
(Xi − X)2
i=1
(Yi −Y )2
i=1
Then weight the ratings
based on the correlation coefficients:
Scorei =
cijr j∈N uj Σ
cijr j∈N uj Σ