Please see also:
An Incremental Graph Pattern Matching Based Dynamic Cold-Start Recommendation Method
PAPER "AN EFFICIENT SIMILARITY-BASED MODEL FOR WEB SERVICE RECOMMENDATION"
PAPER Recommender Systems for the Department of Defense and Intelligence Community
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.
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.
The key to long-lasting romance is STRICT PERSONALITY SIMILARITY, and not "meet other people with similar interests"
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"