The Kernix Lab will talk about the development of a recommender engine at the RecSys MeetUp. We will discuss both strategic and technical considerations for a production ready system. Technically, how we handle cold start, misspelled words and content high renewal rates will be shared.

1. Kernix
digital & data
1
9th Meetup RecSysFr
Recommendation systems at
Kernix
Stanislas Morbieu
PhD candidate in Machine Learning
Cristian Perez
Data Scientist

2. 2
A few words about us...

3. KERNIX DATA LAB 3KERNIX DATA LAB
Kernix
Kernix guide and accelerate companies
from PoC to IT integration in data science
projects.
Especially in risk prediction in the
bank/insurance sector, for Natixis, Euler
Hermes or Allianz.
2
founders
50
employees
600
completed
projects
3,8
millions €
in turnover
10
published
books
17
years of
experience
Data Lab
Digital Factory
Data Lab

4. KERNIX DATA LAB 4
4
Data Lab
Risk prediction :
∙ Allianz UK
∙ Euler Hermes
- Natixis
Marketing Automation :
∙ Radium One
∙ Performics
Predictive maintenance :
∙ SNCF
∙ Enedis
Fraud detection :
∙ Colas
Data Viz :
∙ SolarImpulse
∙ Cop 21

5. Ads recommendation
5

6. Difficulties
● Texts are not well written (typos,
grammar mistakes,
abbreviations)
● High volume and continuously
incoming
● High renewal rate
● Cold start
● Location and price
considerations
6

7. Strategies
7
● Recommendation based on
semantic categories and user
behavior
● Scalable semantic content
based recommendation
● Ongoing research project

8. Recommendation based on semantic categories
and user behavior
8

9. 9
Recommendation based on semantic categories and user
behavior
“Dolls” category
“motorbikes”
category

10. 10
Recommendation based on semantic categories and user
behavior
“Dolls” category
“motorbikes”
category

11. 11
Recommendation based on semantic categories and user
behavior
“Dolls” category
“motorbikes”
category

12. 12
Recommendation based on semantic categories and user
behavior
“Dolls” category
“motorbikes”
category

13. 13
Recommendation based on semantic categories and user
behavior
“Dolls” category
“motorbikes”
category

14. Scalable semantic content based
recommendation
14

15. Recommendation Kernix
15
Process steps:
Formatting (lemmatization, tokenization,...) of the
description and the title
Computing a semantic vector for a classified ad
Computing distances with the most probables
neighbors
Saving relationships in the graph database
On demand, search the most similar classified ads of a
given one

16. Technical Stack
16

17. Pipeline
17

18. Monitoring
18

19. Conclusions
19

20. Conclusions
20
● Each project is unique:
○ Different requirements
○ Several strategies
● Tradeoffs and specific aspects to consider:
○ Text length (short or long text)
○ Take into account user behavior
○ Scalability (high renewal rate)
○ location and price consideration
○ types of content (pictures, text, song…)
● Other recommendation engines:
○ On demand video platform
○ Workshop recommendation
○ Content recommendation
○ Scientific publications
recommendation for a
pharmaceutical company

21. Agence Kernix
6 rue Lalande 75014 Paris
+33 (0)1 53 98 73 40
info@kernix.com
www.kernix.com
Merci
de votre attention
Stanislas Morbieu
PhD in Machine Learning
smorbieu@kernix.com
Cristian Perez
Data scientist
cperez@kernix.com