Teaser and some slides for the presentation by Kevin Heyman, Data Scientist at Van de Velde and Nele Verbiest, Data Scientist at Python Predictions at Data Innovation Summit, June 27, 2018, Brussels. In this story, Kevin and Nele explain how they have build a recommendation engine to predict your next lingerie purchase

1. WE RECOMMEND
YOUR NEXT
LINGERIE PURCHASE
KEVIN
HEYMAN
NELE
VERBIEST
JUNE 27th, Brussels
di-summit.com

2. THE STORY

3. ?the challenge of
this project lies
in offering
valuable
product recom-
mendations in a
digital context

4. THE PROCESS

5. RECOMMENDATIONS
a critical component of many recommendation
systems is a similarity matrix, indicating similarity
between items (=products)

7. PRIMADONNA SUMMER PRIMADONNA MEADOW
Two bras are similar
if they are bought by
the same women.
in this matrix, similarity
can be defined as the
overlap in the women
who buy two products:
large overlap = similar

8. Two bras are similar
if they are bought by
the same women.
ANDRES SARDA - NEPTUNE MARIE JO L’AVENTURE - TOM
in this matrix, similarity
can be defined as the
overlap in the women
who buy two products:
small overlap = unsimilar

9. using the similarity matrix
to generate
recommendations

10. we’ll use the similarity
matrix to detect items
most similar to each
previous purchase, and
assign points to each
recommendation, so we
can recommend the
item with the highest
total score

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OFFLINE EVALUATION
this approach showed a
significant improvement
over recommending the
most popular product
(graph is accurate but exact
figures are confidential)

12. MAIL TEST
INCREASE IN CTO
CONTROL TEST
also in a real-life mail test, the
recommendations proved to
increase click-through rate
significantly when compared to a
control group offering the most
popular product
(graph is accurate but exact figures are
confidential)

13. RECOMMENDATIONS
CURRENT & OLD SEASON
the previous approach only
works for previous seasons,
for which we have purchase
data, but what if we don’t?

14. PRIMADONNA SUMMER PRIMADONNA MEADOW
Two bras are similar
if they are bought by
the same women.
?
MARIE JO ERIKA AW 2018
for a new season we
don’t know who will
purchase so we cannot
calculate the overlap

15. PRIMADONNA DIVINE
to solve this, we created a
deep neural network to
detect similarity between
purchased items and the
new collection
we first trained this network
to classify images from the
new collection correctly

16. MARIE JO SAKURA
next, we apply the network
to previous purchases, so
we use the model to predict
similar items

17. MARIE JO - FLEUR MARIE JO L’AVENTURE - MAI
example of a detected
similarity:
old season -> new season

18. PRIMADONNA TWIST – TOUGH GIRL PRIMADONNA TWIST - CABARET
example of a detected
similarity:
old season -> new season

19. RECOMMENDATIONS
NEW SEASON
RECOMMENDATIONS
CURRENT & OLD SEASON
as a result, we are able to
offer recommendations for
existing seasons but also
new seasons

20. WE RECOMMEND
YOUR NEXT
LINGERIE PURCHASE
BUT WE DO MORE THAN THAT…