Kernix provides data consulting and recommender systems services using graph databases. They helped Hobbystreet connect craftspeople offering workshops to individuals by building a graph database of users, craftspeople, workshops and categories, then generating recommendations based on category similarities, user interests and other factors. For Konbini media, Kernix constructed a graph of articles, authors and categories to power recommendations of other interesting articles on visited pages and help reduce the 90% bounce rate. Kernix recommends improving semantic analysis and assessing recommendation quality through A/B testing or offline evaluation.

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kernixdigital factory + data lab
Flexible recommender systems
based on graphs

2. |
KERNIX
45
co-workers
500projects
2co-founders
3,5M€ revenue
15years
experience
10books
published
Digital factory Data lab
CO-FOUNDERS
Fabrice Métayer and François-Xavier
Bois, two EPITA engineers, gathered
their complementary profiles to create
Kernix in 2001.
ABOUT KERNIX
Kernix’s core business consists in a
digital factory and a data lab.
This double skill allows us to
accompany our clients from upstream
phases (consulting, study, POC) to
downstream phases (industrialization
by production teams).

3. |
3
DATA LAB
Clients Collaborations
EXPERTISE
Data Pipelines
Cop21
TerraRush
Predictive maintenance
ERDF
Data Vizualisation
SolarImpulse
Recommender systems
PriceMinister
WikiDistrict
Clickalto
HobbyStreet
Marketing Automation
Performics
RadiumOne
Open Data
Accessible.net

4. |
• Graph database
– data stored as nodes
• label : “type” of data stored in the node
• properties : collection of information describing
the node
– nodes are linked together by edges
• type : describes the nature of the relation
– query language : allows to perform graph traversals
• Why graph-oriented recommender
systems ?
– gather heterogeneous data in the same structure
– explicitly take advantage of relationships
– "meaningful" for humans
– easy implementation
– fast execution (no training)
GRAPH-ORIENTED RECOMMENDER SYSTEM

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USE CASE 1 : HOBBYSTREET

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Facilitate connections between craftsmen and private individuals
• Craftsmen : propose workshops (different categories, dates, prices)
• Individuals : follow workshops/categories, sign up at workshops
• Hobbystreet : handle registrations, plannings, payments, propose customized suggestions
CONTEXT

7. |
DATA STRUCTURE
User
name
city
Carftman
name
activity
Workshop
name
description
GPS coordinates
Session
date, time
price
status
stock
Category
name
activity
follows
proposes
related to instance of
participates

8. |
SUGGESTIONS : OVERALL STRATEGY
Category
User
Workshop 1
Category 1
Category 2
Workshop 2
Workshop 3
Workshop 4
Similar descriptions
User
Workshop 1
Workshop 2
Workshop 3
Workshop 4
Workshop 5
Workshop 6
from LSA
Similar users
User 1
Workshop 1
Workshop 2
Workshop 3
User 2
User 3
Workshop 4
Workshop 5
Workshop 6
Usim

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USE CASE 2 : KONBINI

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Context
“... multi format media company
producing its own mix of culture, art
and news content. It promotes
online journalism, advocating an
emphasis on pop culture and a
commitment to develop local
emerging talents.”
“... became one of the first
websites to put Social Media
platforms at the heart of their
strategy.”Issue: ~90% bounce rate (users going back after viewing a
page)
Solution: Recommend interesting articles on the visited
pages will help user experience.

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Entities
French posts [693]
Authors [56]
Categories [534] Mexican posts [149]
English posts [417]
Examples of node properties
blog_id: 9
post_id: 217628
post_date: 20151007
slug: rihanna-thinks-rachel...
boost: 0
viewed_count: 0
facebook_count: 148
twitter_count: 0
Multiple web sites [US,
England, Mexic, France]
US posts [364]

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Recommendations principles
For each posts, we will recommend a list of other posts
based on relations shared with the initial post:
- semantic similarity of the contents [LSA]
- number of common categories
- number of common authors
And also on their own properties:
- the freshness
- social counts
- manual boost
Once the graph constructed, these recommendations
can be obtained thanks to a single Cypher query.

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Conclusion and outlook

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Stacks and Workflows
Konbini web siteHobbystreet web site
POST content GET recommendations POST content
Daily cached
recommendadions
GET recommendations
Live recommendation for dynamic
interactions
Cached recommendation for high
availability needs

15. |
Improve semantic analysis:
• exploit similarity of short descriptions (tweets, comments, …). PhD thesis on the subject.
Assess recommendation quality:
• A/B testing but Needs production deployment.
• Offline testing ? No real assessment on the impact of the recommendations performed.
• Rating of pool of testers ?
Outlook

16. THANK YOU !
Kernix Data Lab
+33 (0)1 53 98 73 43
lab@kernix.com