Recommendation Systems in Various Domains."
Please remember the main disadvantage with recommendation engines based on collaborative filtering is when users instead of providing their personal preference try to guess the global preference and they introduce bias in the recommendation algorithm.
Personality Based Recommender Systems are the next generation of recommender systems because they perform far better than Behavioural ones (past actions and pattern of personal preferences)
That is the only way to improve recommender systems, to include the personality traits of their users. They need to calculate personality similarity between users but there are different formulas to calculate similarity.
Recommender systems are morphing to ......... compatibility matching engines,
as the same used in the Online Dating Industry since years, with low success rates, because they mostly use the Big5 to assess personality
and the Pearson correlation coefficient to calculate similarity.
(Personality traits are highly stable in persons over 25 years old to 45 years old)
Which is the RIGHT approach to innovate in the Personality Based Recommender Systems Arena?
The same approach to innovate in the Online Dating Industry == 16PF5 test or similar to assess personality traits and a new method to calculate similarity between quantized patterns.
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.
The market remains enormous!!