This document provides guidance on designing recommendation engine architectures. It discusses collecting user data through instrumentation, learning from user interactions via A/B testing, and determining the context and mechanism of recommendations. The document states that recommendation engines are essentially classification systems and outlines key components of recommendations like familiarity, trust, formality of suggestions, and efficacy that are analogues to social interactions. It recommends considering how to attract and engage users, present recommendations, collect requisite data, learn from users and performance to improve recommendations.

1. RECOMMENDATION
ENGINES
AN ARCHITECTURAL GUIDE
by Timothy Vogel; Sr. Data Architect; Sony Network Entertainment Inc
(SNEI)
tim.vogel@am.sony.com

2. the vagaries of aggregation!
Presentation Science; start with a
joke...

3. ...or two...
the vagaries of personal taste!

4. ...or three or four...
the vagaries of human experience
relative to human taste
relative to human expectations!

5. Semantic Transport
 Collaborative Filtering - tenets of "semantic transport"
 "Automated Collaborative Filtering and Semantic Transports"
 http://www.lucifer.com/~sasha/articles/ACF.html
 "Social Trust Bonds"
 all recommendations depend upon "trust" between the recommender
and target audience.
 behavioral similarity is the "online proxy" for social familiarity between
human beings.
 trust is promoted and diminished by the efficacy of the recommendation
itself via...
 the recommender's actual motivation
 the recommendee's perception of that motivation
 the recomendee's final judgment of the recommendation's "quality"

6. Recommendation Engine Design Questions
and its architectural analog. Have you considered…
 How To Attract "Recommendees"?
 website design and targeting
 How To Proffer the Recommendation?
 interface
 How To Collect the Requisite Data?
 instrumentation/appropriate type and range of transaction-types
 How To "Learn" From The User?
 A-B-(A) testing
 How To "Learn" From Recommendation Performance?
 adding "clicked recommendation" and "ignored recommendation"
transaction type for feedback learning mode
 How to Profit From Your "Service"?
 monetization

7. Conceptual Architecture
You need to decide…
 What is the context of the recommendation?
 overt (move, book, restaurant recommendation site)
 covert (website with pop-ups or directed navigation)
 Who/What is the mechanism of similarity?
 User transaction
 Item similarity (product-type, genre, meta-data, price, etc.)
 Item similarity via text-analysis
 How will the recommendation be made?
 pull – explicit user participation via ratings
 push – implicit user participation via clicks
 How will we collect the data required?
 api's that ask for it
 java-script that captures it
 deep-packet inspection

8. “How to Develop Online Recommendation
Systems
That Deliver Superior Business Performance”
Cognizant Systems
http://www.cognizant.com/InsightsWhitepapers/How-to-Develop-Online-Recommendation-Systems-that-Deliver-Superior-Business-Performance.pdf

9. In Reality, All Recommendation Engines Are
Glorified Classification Systems
Cognizant Systems
http://www.cognizant.com/InsightsWhitepapers/How-to-Develop-Online-Recommendation-Systems-that-Deliver-Superior-Business-Performance.pdf

10. Components of Recommendations
social construct : recommendation engine's facsimile
 Familiarity;
 history of social interaction : transactional history
 Trust;
 similarity of past interactions : shared transactions
 Formality of suggestion;
 deep insight into a friend's dilemma : "precision"
 mere familiarity with a friend's needs : "recall"
 Efficacy;
 advice-request frequency/regularity: click-thru rate
 Learning;
 memory : A-B-(A) test system

11. End with a joke...
Never forget;
Recommendation
is always a
practical
application.

12. ...or two...
Never forget 2;
Decide while
designing the
Recommendati
on System just
what constitutes
the risk of
making a "bad
recommendatio
n".