Saturday, December 9, 2017

PAPER: Compatibility Family Learning for Item Recommendation and Generation

PAPER: Similarity Measures Using Fuzzified Ratings for Collaborative Filtering

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 but there are different formulas to calculate similarity.

In case you did not see, recommender systems are morphing to compatibility matching engines, as the same used in the Online Dating Industry for years, with low success rates until now because they mostly use the Big Five model to assess personality and the Pearson correlation coefficient to calculate similarity.
Please remember: Personality traits are highly stable in persons over 25 years old to 45 years old.

The key to long-lasting romance: COMPATIBILITY is exactly STRICT PERSONALITY SIMILARITY and not "meet other people with similar interests or political views". 

 WorldWide, there are over 5,000 (five thousand) online dating sites
- but no one is using the 16PF5 (or similar) to assess personality of its members!
- but no one calculates similarity with a quantized pattern comparison method!
- but no one can show Compatibility Distribution Curves to each and every of its members!!!
i.e. if you are a man seeking women, to show how compatible you are with a 20,000,000 women database, and to select a bunch of 100 women from 20,000,000 women database.

- but no one is scientifically proven!  No actual online dating site  is "scientifically proven" because no one can prove its matching algorithm can match prospective partners who will have more stable and satisfying relationships (and very low divorce rates) than couples matched by chance, astrological destiny, personal preferences, searching on one's own, or other technique as the control group in a peer reviewed Scientific Paper for the majority (over 90%) of its members.  

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.
Online 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.
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

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