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)
is the only way to improve recommender systems, to include the
personality traits of their users. They need to calculate personality
similarity between users.
In case you had not noticed,
recommender systems are morphing to compatibility matching engines, as
the same used in the Online Dating Industry.
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.
Dating sites have very big databases, in the range of 20,000,000
(twenty million) profiles, so the Big Five model or the HEXACO model are
not enough for predictive purposes. That is why I suggest the 16PF5
test instead and another method to calculate similarity.
High precision in matching algorithms is precisely the key to open the door and leave the infancy of compatibility testing.
offering the NORMATIVE 16PF5 (or similar test measuring exactly the 16
personality factors) for serious dating, it will be impossible to
innovate and revolutionize the Online Dating Industry.
The Online Dating Industry does not need a 10% improvement, a 50% improvement or a 100% improvement. It does need "a 100 times better improvement"
All other proposals are NOISE and perform as placebo.
Please see also:
PAPER Personalized Recommender System based on Friendship Strength in Social Network Services
Very easy to copycat eHarmony, but very difficult to innovate: a 100 times better algorithm than eHarmony.