PAPER: The impact of consumer preferences on the accuracy of collaborative filtering recommender systems
PAPER A Scalable People-to-People Hybrid Reciprocal Recommender Using Hidden Markov Models
PAPER: Tensor Methods and Recommender Systems
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)
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.
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.