Sunday, November 2, 2014

PAPER: Building and Evaluating an Adaptive Real-time Recommender System

Most recommender algorithms in use today are slow to adapt to changes in user preferences. This is because they are focused towards model-building and offline calculation of recommendations. The fact that they require large amounts of information about users before they can make sensible recommendations does not help their case either. This work proposed an adaptive prediction scheme that makes real-time recommendations to users.

Please read:
Twitter teams with IBM for business analytics

 What comes after the Social Networking wave?
The Next Big Investment Opportunity on the Internet will be .... Personalization!
Personality Based Recommender Systems and Strict Personality Based Compatibility Matching Engines for serious Online Dating with the normative 16PF5 personality test.
If you want to be first in the "personalization arena" == Personality Based Recommender Systems, you should understand HOW TO INNOVATE in the ............ Online Dating Industry first of all!  

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