"Each to Each Programmer's Reference Manual"
Paul McJones and John DeTreville
Note #1997-023. October 1, 1997.

Each to Each applies collaborative filtering techniques to the 
problem of making subjective recommendations to consumers faced 
with "infoglut". The basic idea is to ask people to vote for 
items on a numeric scale, then perform a statistical analysis 
of the collection of all people's votes, and use the results 
of the analysis to predict additional items of potential interest 
to a particular person. Unlike some competitive approaches, the 
Each to Each technology separates prediction from analysis, allows 
predictions to be made using compact "models" produced by the 
analysis, and provides meaningful predictions after a person 
has provided just a few votes.  This manual documents the Each 
to Each APIs and shows how to use them in a complete recommendation 
application.