PAPER: MATCHING THEORY-BASED RECOMMENDER SYSTEMS IN ONLINE DATING

 

 

https://arxiv.org/pdf/2208.11384.pdf

summary of recommender systems  Wednesday, November 24, 2010
https://onlinedatingsoundbarrier.blogspot.com/2010/11/summary-of-recommender-systems.html

RecSys 2021 15th ACM Conference on Recommender Systems
https://onlinedatingsoundbarrier.blogspot.com/2021/09/recsys-2021-15th-acm-conference-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)
https://onlinedatingsoundbarrier.blogspot.com/2019/05/paper-beyond-personalization-research.html
 
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 notice: 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.

Breaking "the online dating sound barrier" is to achieve at least:
3 most compatible persons in a 100,000 persons database.
12 most compatible persons in a 1,000,000 persons database.
48 most compatible persons in a 10,000,000 persons database.
100 times better than Compatibility Matching Algorithms used by actual online dating sites!
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.  

 

PAPER: Predicting Online Shopping Inclination Based on Personality Characteristics

Predicting Online Shopping Inclination Based on Personality Characteristics: HEXACO Traits Model

PAPER: Reciprocal Recommendation: matching users with the right users
https://onlinedatingsoundbarrier.blogspot.com/2020/07/paper-reciprocal-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)
https://onlinedatingsoundbarrier.blogspot.com/2019/05/paper-beyond-personalization-research.html
 
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 notice: 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.

Breaking "the online dating sound barrier" is to achieve at least:
3 most compatible persons in a 100,000 persons database.
12 most compatible persons in a 1,000,000 persons database.
48 most compatible persons in a 10,000,000 persons database.
100 times better than Compatibility Matching Algorithms used by actual online dating sites!
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. 

PAPER: Reciprocal Recommendation: matching users with the right users

http://dsrs-lab.ugr.es/wp-content/uploads/2020/05/SIGIR_Tutorial_RR.pdf

chapter: Personalization 3.0: How Personality Can Predict Consumer Behavior
https://onlinedatingsoundbarrier.blogspot.com/2020/05/chapter-personalization-30-how.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)
https://onlinedatingsoundbarrier.blogspot.com/2019/05/paper-beyond-personalization-research.html
 
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 notice: 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.

Breaking "the online dating sound barrier" is to achieve at least:
3 most compatible persons in a 100,000 persons database.
12 most compatible persons in a 1,000,000 persons database.
48 most compatible persons in a 10,000,000 persons database.
100 times better than Compatibility Matching Algorithms used by actual online dating sites!
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. 

chapter: Personalization 3.0: How Personality Can Predict Consumer Behavior

https://www.igi-global.com/chapter/personalization-30/254698

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)
https://onlinedatingsoundbarrier.blogspot.com/2019/05/paper-beyond-personalization-research.html
 
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 notice: 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.

Breaking "the online dating sound barrier" is to achieve at least:
3 most compatible persons in a 100,000 persons database.
12 most compatible persons in a 1,000,000 persons database.
48 most compatible persons in a 10,000,000 persons database.
100 times better than Compatibility Matching Algorithms used by actual online dating sites!
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. 

PAPER: EVALUATING RECOMMENDER SYSTEMS FOR AI-DRIVEN DATA SCIENCE

https://arxiv.org/pdf/1905.09205.pdf

eHarmony is developing an A.I. feature
http://onlinedatingsoundbarrier.blogspot.com/2019/05/eharmony-is-developing-ai-feature.html

How Hinge used machine learning to build its first online recommendation engine ?
http://onlinedatingsoundbarrier.blogspot.com/2019/05/how-hinge-used-machine-learning-to.html

Scientists are studying your tweets, YouTube videos, Instagram posts. Is that ethical?
https://onlinedatingsoundbarrier.blogspot.com/2019/04/scientists-are-studying-your-tweets.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)
https://onlinedatingsoundbarrier.blogspot.com/2019/05/paper-beyond-personalization-research.html
 
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 notice: 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.

Breaking "the online dating sound barrier" is to achieve at least:
3 most compatible persons in a 100,000 persons database.
12 most compatible persons in a 1,000,000 persons database.
48 most compatible persons in a 10,000,000 persons database.
100 times better than Compatibility Matching Algorithms used by actual online dating sites!
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.

PAPER: Beyond Personalization: Research Directions in Multistakeholder Recommendation

https://arxiv.org/pdf/1905.01986.pdf

The NEW era of Personality Based Recommender Systems
https://onlinedatingsoundbarrier.blogspot.com/2010/10/new-era-of-personality-based.html

Our apps are an extension of our personalities, new research reveals
https://onlinedatingsoundbarrier.blogspot.com/2019/05/our-apps-are-extension-of-our.html

A Cultural Algorithm for Determining Similarity Values Between Users in Recommender Systems
https://onlinedatingsoundbarrier.blogspot.com/2019/04/a-cultural-algorithm-for-determining.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 notice: 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.

Breaking "the online dating sound barrier" is to achieve at least:
3 most compatible persons in a 100,000 persons database.
12 most compatible persons in a 1,000,000 persons database.
48 most compatible persons in a 10,000,000 persons database.
100 times better than Compatibility Matching Algorithms used by actual online dating sites!
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.

PAPER: BlindDate recommender: A context-aware ontology-based dating recommendation platform

http://journals.sagepub.com/doi/abs/10.1177/0165551518806114


Online dating could be worth USD 12 Billion — and it's all thanks to Tinder ???
https://onlinedatingsoundbarrier.blogspot.com/2018/10/online-dating-could-be-worth-usd-12.html


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.
 
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.

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

about 12th ACM Conference on Recommender Systems

Vancouver, Canada, 2nd-7th October 2018

http://www.recsyschallenge.com/2018/
https://recsys.acm.org/recsys18/
http://www.recsyschallenge.com/2018/#program

Please see also
https://onlinedatingsoundbarrier.blogspot.com/2018/10/eharmony-2007-2018-research.html
https://onlinedatingsoundbarrier.blogspot.com/2017/05/paper-improved-collaborative-filtering.html
https://onlinedatingsoundbarrier.blogspot.com/2016/09/tinder-will-use-spotify-to-show-your.html
https://onlinedatingsoundbarrier.blogspot.com/2016/09/proceedings-of-4th-workshop-on-emotions.html
https://onlinedatingsoundbarrier.blogspot.com/2016/09/human-computer-interaction-hci-for.html

and notice 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 == 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.
 
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.

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   

PAPER: A Comparative Study of People-to-People Recommender Algorithms in Hybrid Method

A Comparative Study of People-to-People Recommender Algorithms in Hybrid Method

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 notice, recommender systems are morphing to compatibility matching engines, as the same used in the Online Dating Industry for years, with low success rates!!!  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.

Online Dating sites OFFERING COMPATIBILITY MATCHING METHODS BASED ON PERSONALITY SIMILARITY 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 normative personality test instead.
The same applies for Personality Based Recommender Systems.
 
Please see also:
People-to-People Reciprocal Recommenders
http://onlinedatingsoundbarrier.blogspot.com.ar/2015/11/people-to-people-reciprocal-recommenders.html
A people-to-people content-based reciprocal recommender using hidden markov models
http://dl.acm.org/citation.cfm?id=2507214&dl=ACM&coll=DL&CFID=589669483&CFTOKEN=18383938
How AI can help you find a date ? 
http://onlinedatingsoundbarrier.blogspot.com.ar/2017/01/how-ai-can-help-you-find-date.html
PAPER: Tensor Methods and Recommender Systems
http://onlinedatingsoundbarrier.blogspot.com.ar/2016/03/paper-tensor-methods-and-recommender.html
Artificial Intelligence to Help You Meet Your Soulmate ?
http://onlinedatingsoundbarrier.blogspot.com.ar/2016/12/artificial-intelligence-to-help-you.html

PAPER: An Improved Trust-aware Recommender System for Personalized User Recommendation

An Improved Trust-aware Recommender System for Personalized User Recommendation in Tmall

 

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 notice, recommender systems are morphing to compatibility matching engines, as the same used in the Online Dating Industry for years, with low success rates!!! 
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.

Online Dating sites OFFERING COMPATIBILITY MATCHING METHODS BASED ON PERSONALITY SIMILARITY 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 normative personality test instead.
The same applies for Personality Based Recommender Systems.

Please see:
PAPER: A Recommender System based on an Improved Simultaneous Selection Method of Query Items and Neighbors
http://onlinedatingsoundbarrier.blogspot.com.ar/2016/12/paper-recommender-system-based-on.html 

PAPER: A Recommender System based on an Improved Simultaneous Selection Method of Query Items and Neighbors

[PDF] A Recommender System based on an Improved Simultaneous Selection Method of Query Items and Neighbors

 

 

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 notice, recommender systems are morphing to compatibility matching engines, as the same used in the Online Dating Industry for years, with low success rates!!! because they mostly use the Big 5 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.

Online Dating sites OFFERING COMPATIBILITY MATCHING METHODS BASED ON PERSONALITY SIMILARITY 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 normative personality test instead.
The same applies for Personality Based Recommender Systems.
 
  
Very easy to copycat eHarmony, but very difficult to innovate: a 100 times better algorithm than eHarmony.
http://onlinedatingsoundbarrier.blogspot.com.ar/2016/10/very-easy-to-copycat-eharmony-but-very.html

PAPER: Social Recommendation with Strong and Weak Ties

http://dl.acm.org/citation.cfm?id=2983701

With the explosive growth of online social networks, it is now well understood that social information is highly helpful to recommender systems. Social recommendation methods are capable of battling the critical cold-start issue, and thus can greatly improve prediction accuracy. The main intuition is that through trust and influence, users are more likely to develop affinity toward items consumed by their social ties. Despite considerable work in social recommendation, little attention has been paid to the important distinctions between strong and weak ties, two well-documented notions in social sciences. In this work, we study the effects of distinguishing strong and weak ties in social recommendation. We use neighbourhood overlap to approximate tie strength and extend the popular Bayesian Personalized Ranking (BPR) model to incorporate the distinction of strong and weak ties. We present an EM-based algorithm that simultaneously classifies strong and weak ties in a social network w.r.t. optimal recommendation accuracy and learns latent feature vectors for all users and all items. We conduct extensive empirical evaluation on four real-world datasets and demonstrate that our proposed method significantly outperforms state-of-the-art pairwise ranking methods in a variety of accuracy metrics.

Please see:
PAPER Identifying Opportunities for Valuable Encounters: Toward Context-Aware Social Matching Systems
http://onlinedatingsoundbarrier.blogspot.com.ar/2015/07/paper-toward-context-aware-social.html

PAPER: Similarity Metrics from Social Network Analysis for Content Recommender Systems
http://onlinedatingsoundbarrier.blogspot.com.ar/2016/10/paper-similarity-metrics-from-social.html

PAPER: INNOVATIVE PERSONALITY-BASED DIGITAL SERVICES
http://onlinedatingsoundbarrier.blogspot.com.ar/2016/09/paper-innovative-personality-based.html

PAPER: Romantic Partnerships and the Dispersion of Social Ties
http://onlinedatingsoundbarrier.blogspot.com.ar/2013/11/paper-romantic-partnerships-and.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.

In case you had not noticed, recommender systems are morphing to compatibility matching engines, as the same used in the Online Dating Industry.   
 
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