PAPER: A Study on the Accuracy of Prediction in Recommendation System Based on Similarity Measures

[PDF] A Study on the Accuracy of Prediction in Recommendation System Based on Similarity Measures

PAPER: Improved collaborative filtering recommendation algorithm of similarity measure
https://onlinedatingsoundbarrier.blogspot.com/2017/05/paper-improved-collaborative-filtering.html

PAPER: An Efficient Similarity Measure for User-based Collaborative Filtering Recommender Systems Inspired by the Physical Resonance Principle
https://onlinedatingsoundbarrier.blogspot.com/2017/12/paper-efficient-similarity-measure-for.html

PAPER: A Modified Similarity Measure for Improving Accuracy of User-Based Collaborative Filtering
https://onlinedatingsoundbarrier.blogspot.com/2018/06/paper-modified-similarity-measure-for.html

about 12th ACM Conference on Recommender Systems
https://onlinedatingsoundbarrier.blogspot.com/2018/10/about-12th-acm-conference-on.html

PAPER: Personalized recommendation via user preference matching
https://onlinedatingsoundbarrier.blogspot.com/2019/02/paper-personalized-recommendation-via.html

PAPER: Similarity Measures Using Fuzzified Ratings for Collaborative Filtering
https://onlinedatingsoundbarrier.blogspot.com/2017/12/paper-similarity-measures-using.html

PAPER "A New Similarity Measure Based on Mean Measure of Divergence for Collaborative Filtering in Sparse Environment"
https://onlinedatingsoundbarrier.blogspot.com/2016/08/paper-new-similarity-measure-based-on.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.
http://onlinedatingsoundbarrier.blogspot.com.ar/2017/01/paper-comparative-study-of-people-to_14.html  

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".  

Which is the RIGHT approach to innovate in the Personality Based Recommender Systems Arena? 
The same approach to innovate in the Online Dating Industry == 16PF6 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 16PF6 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 16PF6 (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 8 tips to innovate in the Online Dating Industry!
http://onlinedatingsoundbarrier.blogspot.com.ar/2013/12/the-8-tips-to-innovate-in-online-dating.html