PAPER A Preference-Driven Database Approach to Reciprocal User Recommendations in Online Social Networks

presented at DEXA 2016 : 27th International Conference on Database and Expert Systems Applications
http://dexa.org/accepted_papers/623
http://link.springer.com/chapter/10.1007/978-3-319-44406-2_1

Please see:
PAPER: A Personalized Recommendation Algorithm with User Trust in Social Network 
http://onlinedatingsoundbarrier.blogspot.com.ar/2016/08/paper-personalized-recommendation.html

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.


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"