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".
