The document discusses crowd-sourcing wine recommendations using a dataset of 130,000 wine reviews from 82,342 unique wines reviewed by 655 users. It describes using latent semantic analysis on the reviews to find similar wines and evaluate recommendation accuracy through cross-validation. Various text processing and natural language techniques are applied to the reviews to improve recommendations, and future directions are proposed like incorporating keyword search and analyzing reviews by region.

1. GrapeVin
Anasuya Das
Insight Data Science

2. Can wine recommendations be crowd sourced?

3. Can wine recommendations be crowd sourced?
130,000 reviews
82,342 unique wines
655 unique users
Images from:

4. Can wine recommendations be crowd sourced?
http://GrapeVin.us
130,000 reviews
82,342 unique wines
655 unique users
Images from:

5. Content based recommender

6. Content based recommender
Deep ruby color leads to a nose of currant and cedar
chest. The complex body contains plum,black cherry
and just the right amount of oak.

7. Content based recommender
Deep ruby color leads to a nose of currant and cedar
chest. The complex body contains plum,black cherry
and just the right amount of oak.
Get most similar wines in
latent semantic space

8. Content based recommender
Deep ruby color leads to a nose of currant and cedar
chest. The complex body contains plum,black cherry
and just the right amount of oak.
Deep ruby color, currant, dark cherry, vanilla,
cedar, full bodied, oak
Get most similar wines in
latent semantic space

9. 2/8/2015 mpld3 plot
-0.6 -0.4 -0.2 -0.0 0.2 0.4 0.6 0.8
-0.6
-0.4
-0.2
-0.0
0.2
0.4
0.6
0.8
Pinot noir
Champagne
component3
component 2
Bordeaux
Wine reviews cluster by the type of wine

10. Preserved're(notopic'organiza(on'in'early'visual'cortex'followin
'implica(ons'for'visual'func(on?'
How*does*V1*damage*affect*the*re;notopic*organiza;on*of*spared*
visual*cortex?**How*is*this*related*to*spared*visual*func;on?*
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Background&
Anasuya&Das1,2,&Elisha&P.&Merriam3,&David&J.&Heeger3,&Krystel&R.&
1Flaum&Eye&Ins+tute&and&2Centre&for&Visual&Science,&University&of&Rochester,&3Center&for&N
Damage&to&the&primary&visual&cortex&(V1)&or&its&afferents&produces&a&severe&
loss& of& vision& in& the& contralateral& visual& hemifield& (VF),& called& cor+cal&
blindness& (CB).& Studies& in& nonGhuman& primates& with& lesions& to& V1& observe&
reduced& but& organized& re+notopic& ac+vity& in& the& lesioned& visual& cortex&
(Schmid,&2009&&&2010).&These&and&other&studies&suggest&that&residual&visual&
processing&is&mediated&by&either&spared&V1&or&extraGgeniculoGcalcarine&input&
to&extrastriate&visual&areas&(reviewed&in&Das&and&Huxlin,&2010).&Human&fMRI&
studies&of&CB&have&examined&single&subjects,&and&the&lesion&characteris+cs&of&
these&subjects&have&varied&across&studies.&The&re+notopic&organiza+on&of&the&
damaged&visual&cortex&in&humans&is&not&well&characterized.&
Group*1:*Re;notopic*organiza;on*is*preserved*around*lesion**
But$extrastriate$cortex$has$greater$representa1on$of$central$field$than$spared$V1(CB8)$&
Intact&hemisphere&Damaged&hemisphere& Damaged&hemisphere&
Ques;ons& V2d'
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The story so far …
y*visual*cortex*is*

11. Questions?

13. Cross validation: How accurately can the star rating of
a recommended wine be predicted?
red,spicy, oak
Train classifier
Get similar wines
Predict ratings

14. Cross validation: How accurately can the star rating of
a recommended wine be predicted?
Train a Naive-Bayes to learn the user preference of each user.
Test on the reviews written by other user for recommended wines

15. Cross validation: How accurately can the star rating of
a recommended wine be predicted?
2/8/2015 mpld3 plot
1 2 3 4 5
0
10
20
30
40
50
60
Rating prediction accuracy
Numberofusers
0.2 0.4 0.6 0.8 1
Train a Naive-Bayes to learn the user preference of each user.
Test on the reviews written by other user for recommended wines

16. A
L
G
O
R
I
T
H
M
Remove stop words
Stem using wordNet
Synsets to defeat adverbs
Detect language and filter
Unicode, HTML scrubbing
Cosine similarity in lower dimensional
space
reviews
= n
Truncated SVD, a.k.a Latent Semantic Indexing on
132,000 reviews x 20,000 words
TfIdf - m columns
x11 x12 ….
x21 x22 ….
x31 x32 ….
~
n x r components
~
x11 x12 ….
x21 x22 ….
x31 x32 ….
Recommend top 10
most similar and
highest rated wines

17. 2/8/2015 mpld3 plot
1 2 3 4 5
0
10
20
30
40
50
60
Crossvalidation: What is the probability of
recommending a wine that is already reviewed
P of recommending already reviewed wine
Numberofusers
0.2 0.4 0.6 0.8 1
Data:
SuperUsers: Top 100 users with the most reviews
Wines: Top 100 wines reviewed by SuperUsers
Method: For each wine reviewed by SuperUser
recommend 20 most similar wines based on
remaining 99 users

18. 1. convert to lower case
2. remove stop words and punctuation and html code
3. deal with broken unicode characters and replace with plain text
4. detect language and only include reviews in english
5. lemmatize using wordNet- works only on nouns and adjectives
6. do synset and pertainyms to convert adverbs to adjectives
7. use bigrams
8. tokenize using term frequence- inverse document frequency
Text processing steps

19. 1/29/2015 mpld3 plot
500 1,000 1,500 2,000 2,500 3,000
20
30
40
50
60
70
80
90
Explainedvariance(%)
number of components
Selecting k components

20. 1. Incorporate keyword search
2. Scale up and increase inventory
3. Scrape wine pricing information and local availability
4. Analyze tasting notes by vineyard or geographical region
- does soil and climate really impact how wines taste
Future directions