PAPER Recommender Systems with Personality
from RecSys ’16, September 15 - 19, 2016, Boston , MA, USA
PAPER Partially Observable Markov Decision Process for Recommender Systems
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
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)
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.
Without 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.