The document outlines an architecture for a recommendation engine that first extracts user information through adapters, uses an AI system called AIRS to compute recommendations from user profiles offline, and then serves those recommendations to users online through activators in a non-intrusive way to deliver personalized products and content. The recommendation engine aims to understand user preferences from behaviors to provide the right products and web pages to visitors.

1. INTRODUCTION
• To deliver the right product to the • To know the products from their
right visitor features
• To know the visitors’ preferences • To determine « types of products »
from their behaviours
• To build visitors affinities

2. ARCHITECTURAL OVERVIEW OF OUR RECOMMENDATION ENGINE
• Learn Tool: centerpiece of our • Suitability assured by Adapters
product • Outputs served by Activators
• Recommendations performed by
Experts

3. MAIN PHASES PERFORMED BY OUR SYSTEM
Extarction
of information Creation of
From recommendations Storing of the Online use of the
the available by appropriated recommendations recommendations
data experts
• Encapsulation of all kind of available • Non-intrusive storing of the
information about visitors done by recommendations
adaptors • Online use of the stored
• Computation of recommendations recommendations through Activators
from these encapsulations (we call
DNAs) by AiRS

4. Technical View: Main Process
"click"
Log of actions
Time, ip, userid, action, OS, ...
Browse
Processing
Cookie
User DNAs

5. Multi-Classification
products/ads
Multi-valued
multi-category
Web pages system
Keyword search

6. Off-line Learning
User DNAs
AIRS: AI engine
Kinds of users
and A user
Kind of user
preference profiles

7. Learning Result
Kind of user
preference profile:
Categories Score
Clothes 0.9
sport 0.7
DVD 0.6

8. Online Recommendation
User Mixture of
experts
AIRS online a) exploitation experts
engine b) exploration experts
c) feature-based expert
e.g., ads color, type, etc.
Recommendations d) context-based expert
e.g., hour of the day

9. IMPLEMENTATION OF THE AIRS SYSTEM
Adapter Expert
DNA Recommendations
4
Off Line
2
3 1
Web
Behavioral Data Activator
From web Application
On Line
• Online processes: • Offline processes
 Call to the Activator  Extraction of behavioural data
 Storing of behavioural data  Computation of
recommendations

10. AIRS VIEWER
• Control Panel: • Other viewers:
 Profiles of the audience  extensibility
 Key Metrics