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: Yelp Recommendation System via User’s Personality

and Sentiment Analysis in Reviews
 

http://rafaelsilva.com/wp-content/uploads/2018/12/006-User-Personality-Sentiment-Analysis.pdf

PAPER: RecSys Challenge 2018: playlist continuation based on shared neighborhood-methods, matrix factorization and audio-feature classification
https://onlinedatingsoundbarrier.blogspot.com/2018/10/paper-recsys-challenge-2018-playlist.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 == 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 

People-to-People Reciprocal Recommenders

Chapter of Recommender Systems Handbook pp 545-567
http://link.springer.com/chapter/10.1007/978-1-4899-7637-6_16

Abstract

People-to-people reciprocal recommenders are an emerging class of recommender systems. They differ from traditional items-to-people recommenders as they must satisfy the preferences and needs of the two parties involved in the recommendation. In contrast, traditional items-to-people recommenders are one-sided and must satisfy only the preference of the person for whom the recommendation is generated. We review the characteristics and present an overview of existing reciprocal recommenders. To illustrate the various aspects of these recommenders and how reciprocity can be taken into account in building and evaluating such recommenders, we present a case study in online dating. We describe our reciprocal recommender algorithm that combines content-based and collaborative filtering and uses data from both user profiles and user interactions. We also study the differences between the implicit and explicit user preferences and show that implicit preferences, learned from user interactions, are better predictors of successful interactions. We conclude by outlining some future research directions.

PAPER A Deployed People-to-People Recommender System in Online Dating
http://onlinedatingsoundbarrier.blogspot.com.ar/2015/10/paper-deployed-people-to-people.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. Oh that is exactly ............ guess ............. yes ........ LIFEPROJECT METHOD, ready since 2001!
All other proposals are NOISE and perform as placebo.
 
Please read: 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 "Homogeneity of personal values and personality traits in Facebook social networks" 
http://onlinedatingsoundbarrier.blogspot.com.ar/2015/11/thesis-social-network-based-recommender.html

PAPER Recommender Systems supporting Decision Making through Analysis of User Emotions and Personality 
http://onlinedatingsoundbarrier.blogspot.com.ar/2015/11/paper-homogeneity-of-personal-values.html
ACM RecSys CrowdRec 2015 Workshop  
http://onlinedatingsoundbarrier.blogspot.com.ar/2015/10/acm-recsys-crowdrec-2015-workshop.html

PAPER Recommender Systems supporting Decision Making through Analysis of User Emotions and Personality

Recommender Systems supporting Decision Making through Analysis of User Emotions and Personality

"Abstract. The influence of emotions in decision making is a popular research topic in psychology and cognitive studies. A person facing a choosing problem has to consider different solutions and take a decision. During this process several elements in
fluence the reasoning, some of them are rational, others are irrational, such as emotions.
Recommender Systems can be used to support decision making by narrowing the space of options. Typically they do not consider irrational elements during the computational process, but recent studies show that accuracy of suggestions improves whether user's emotional state is included in the recommendation process.
In this paper we propose the idea of defining a framework for an Emotion-Aware Recommender System. The user emotions will be formalized in an a ective user profile which can act as an emotional computational model. The Recommender System will
use the affective profile integrated with case base reasoning to compute recommendations."

presented at 14th Conference of the Italian Association for Artificial Intelligence, Ferrara
http://aixia2015.unife.it/dc-accepted-papers/

http://www.slideshare.net/MarcoPolignano/recommender-systems-supporting-decision-making-through-analysis-of-user-emotions-and-personality

PAPER A Deployed People-to-People Recommender System in Online Dating
http://onlinedatingsoundbarrier.blogspot.com.ar/2015/10/paper-deployed-people-to-people.html
EMPIRE 2013: Emotions and Personality in Personalized Services
http://onlinedatingsoundbarrier.blogspot.com.ar/2013/06/empire-2013-emotions-and-personality-in.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)

http://onlinedatingsoundbarrier.blogspot.com.ar/2015/08/paper-personalized-recommendation.html 
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. Oh that is exactly ............ guess ............. yes ........ LIFEPROJECT METHOD, ready since 2001!
All other proposals are NOISE and perform as placebo.

The 9th ACM Conference on Recommender Systems soon!

http://recsys.acm.org/recsys15/program/
http://onlinedatingsoundbarrier.blogspot.com.ar/2015/03/the-9th-acm-conference-on-recommender.html

Vienna, Austria, 16th-20th September 2015
http://recsys.acm.org/recsys15/

Please remember:
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)
http://onlinedatingsoundbarrier.blogspot.com.ar/2015/08/paper-personalized-recommendation.html
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.
If you want to be first in the "personalization arena" == Personality Based Recommender Systems, you should understand HOW TO INNOVATE in the ............ Online Dating Industry first of all!
The next wave of innovation!
http://onlinedatingsoundbarrier.blogspot.com.ar/2013/06/the-next-wave-of-innovation.html
What comes after the Social Networking wave?
The Next Big Investment Opportunity on the Internet will be .... Personalization!
Personality Based Recommender Systems and Strict Personality Based Compatibility Matching Engines for serious Online Dating with the normative 16PF5 personality test.  

PAPER Reciprocal Recommendation System for Online Dating

http://arxiv.org/abs/1501.06247
http://www.cs.uml.edu/~pxia/
http://arxiv.org/pdf/1501.06247v2.pdf

related to the PAPER Predicting User Replying Behavior on a Large Online Dating Site
http://onlinedatingsoundbarrier.blogspot.com.ar/2014/05/predicting-user-replying-behavior-on.html
Same authors of the paper Who is Dating Whom: Characterizing User Behaviors of a Large Online Dating Site.
But, unfortunately ..... big data dating IS NOT the key to long-lasting romance  http://onlinedatingsoundbarrier.blogspot.com.ar/2014/03/big-data-dating-is-not-key-to-long.html 

 Please read: Matching Algorithms for the Online Dating Industry 2014 (serious daters)
http://onlinedatingsoundbarrier.blogspot.com.ar/2014/01/matching-algorithms-for-online-dating.html

and PAPER: Cooperative Query Personalization Based on Perceptual Similarity
http://onlinedatingsoundbarrier.blogspot.com.ar/2015/02/paper-cooperative-query-personalization.html

Please remember:
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. 
Similarity is a word that has different meanings for different persons or companies, it exactly depends on how mathematically is defined. In case you had not noticed, recommender systems are morphing to .......... compatibility matching engines, as the same used in the Online Dating Industry since years, with low success rates until now because they mostly use the BIG 5 to assess personality and the Pearson correlation coefficient to calculate similarity.
The BIG 5 (Big Five) normative personality test is obsolete. The HEXACO (a.k.a. Big Six) is another oversimplification. Online Dating sites have very big databases, in the range of 20,000,000 (twenty million) profiles, so the BIG 5 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. I calculate similarity in personality patterns with (a proprietary) pattern recognition by correlation method. It takes into account the score and the trend to score of any pattern. Also it takes into account women under hormonal treatment because several studies showed contraceptive pills users make different mate choices, on average, compared to non-users. "Only short-term but not long-term partner preferences tend to vary with the menstrual cycle".

If you want to be first in the "personalization arena" == Personality Based Recommender Systems, you should understand the ............ Online Dating Industry first of all!

Please see: "How to calculate personality similarity between users"
Short answer: the key is the ENSEMBLE!
(the whole set of different valid possibilities)
http://onlinedatingsoundbarrier.blogspot.com.ar/2013/03/how-to-calculate-personality-similarity.html
Worldwide there are over 5,000 online dating sites, no one uses the 16PF5, no one is scientifically proven yet, and no one can show you compatibility distribution curves, 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.
What comes after the Social Networking wave?
The Next Big Investment Opportunity on the Internet will be .... Personalization!
Personality Based Recommender Systems and Strict Personality Based Compatibility Matching Engines for serious Online Dating with the normative 16PF5 personality test. 

PAPER: Collaborative filtering recommendation based on conditional probability and weight adjusting

http://www.inderscience.com/info/inarticle.php?artid=67073

Abstract: Collaborative filtering recommendation algorithm is one of the most successful technologies for building recommender systems. However, a user-based collaborative filtering method has its limits related to similarity and ratings. To avoid those limits, we propose a new item-based collaborative filtering algorithm based on conditional probability and weight adjusting in this paper. At first, any two items are selected to compute the similarity from common user ratings, and only the items with the similarity greater than preset thresholds are chosen as the set of supporting items. Then an integral parameter, that is the frequency of two items present simultaneously, is used to adjusted similarity weights. Finally, a new algorithm combining conditional probability and weight adjusting is proposed to predict ratings. The experimental results show the proposed algorithm is feasibleand effective in practice.

Please see also:
Paper: Recommendation Systems for Markets with Two Sided Preferences
http://onlinedatingsoundbarrier.blogspot.com.ar/2015/01/paper-recommendation-systems-for.html
PAPER Collaborative filtering for people-to-people recommendation in online dating: data analysis and user trial
http://onlinedatingsoundbarrier.blogspot.com.ar/2014/12/paper-collaborative-filtering-for.html

Please remember:
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)
http://onlinedatingsoundbarrier.blogspot.com.ar/2014/11/paper-investigation-and-application-of.html
http://onlinedatingsoundbarrier.blogspot.com.ar/2014/03/new-papers-recommender-systems.html

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. 
Similarity is a word that has different meanings for different persons or companies, it exactly depends on how mathematically is defined. In case you had not noticed, recommender systems are morphing to .......... compatibility matching engines, as the same used in the Online Dating Industry since years, with low success rates until now because they mostly use the BIG 5 to assess personality and the Pearson correlation coefficient to calculate similarity.
The BIG 5 (Big Five) normative personality test is obsolete. The HEXACO (a.k.a. Big Six) is another oversimplification. Online Dating sites have very big databases, in the range of 20,000,000 (twenty million) profiles, so the BIG 5 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. I calculate similarity in personality patterns with (a proprietary) pattern recognition by correlation method. It takes into account the score and the trend to score of any pattern. Also it takes into account women under hormonal treatment because several studies showed contraceptive pills users make different mate choices, on average, compared to non-users. "Only short-term but not long-term partner preferences tend to vary with the menstrual cycle".

If you want to be first in the "personalization arena" == Personality Based Recommender Systems, you should understand the ............ Online Dating Industry first of all!

Please see: "How to calculate personality similarity between users"
Short answer: the key is the ENSEMBLE!
(the whole set of different valid possibilities)
http://onlinedatingsoundbarrier.blogspot.com.ar/2013/03/how-to-calculate-personality-similarity.html
Worldwide there are over 5,000 online dating sites, no one uses the 16PF5, no one is scientifically proven yet, and no one can show you compatibility distribution curves, 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.

Please read also
An exercise of similarity.
How LIFEPROJECT METHOD calculates similarity.
STRICT PERSONALITY SIMILARITY by LIFEPROJECT METHOD.
Personality Distribution Curves using the NORMATIVE 16PF5.
ALGORITHMS & POWER CALCULATION.
Innovations: to take the 16PF5 test 3 times.
Why your brain distorts!

PAPER Measuring similarity between user profile and library book

PAPER Measuring similarity between user profile and library book.

In the development of recommender system either the content or collaborative filtering is necessary. To filter the records it is required to measure the similarity between profile of user and items present in the dataset. This experiment is performed on the dataset containing 978 books related to computer science field and 7 users. Similarity between profile of user and contents of book is measured using Euclidean, Manhattan, Minkowski, Cosine distances. The results are evaluated and compared. This work is useful in the development of library recommender system.
--------------------------------------
 Please remember:
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)
http://onlinedatingsoundbarrier.blogspot.com.ar/2014/11/paper-investigation-and-application-of.html
http://onlinedatingsoundbarrier.blogspot.com.ar/2014/03/new-papers-recommender-systems.html

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. 
Similarity is a word that has different meanings for different persons or companies, it exactly depends on how mathematically is defined. In case you had not noticed, recommender systems are morphing to .......... compatibility matching engines, as the same used in the Online Dating Industry since years, with low success rates until now because they mostly use the BIG 5 to assess personality and the Pearson correlation coefficient to calculate similarity.
The BIG 5 (Big Five) normative personality test is obsolete. The HEXACO (a.k.a. Big Six) is another oversimplification. Online Dating sites have very big databases, in the range of 20,000,000 (twenty million) profiles, so the BIG 5 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. I calculate similarity in personality patterns with (a proprietary) pattern recognition by correlation method. It takes into account the score and the trend to score of any pattern. Also it takes into account women under hormonal treatment because several studies showed contraceptive pills users make different mate choices, on average, compared to non-users. "Only short-term but not long-term partner preferences tend to vary with the menstrual cycle".

If you want to be first in the "personalization arena" == Personality Based Recommender Systems, you should understand the ............ Online Dating Industry first of all!

Please see: "How to calculate personality similarity between users"
Short answer: the key is the ENSEMBLE!
(the whole set of different valid possibilities)
http://onlinedatingsoundbarrier.blogspot.com.ar/2013/03/how-to-calculate-personality-similarity.html
Worldwide there are over 5,000 online dating sites, no one uses the 16PF5, no one is scientifically proven yet, and no one can show you compatibility distribution curves, 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.
 
Please read also
An exercise of similarity.
How LIFEPROJECT METHOD calculates similarity. 
STRICT PERSONALITY SIMILARITY by LIFEPROJECT METHOD.
Personality Distribution Curves using the NORMATIVE 16PF5.
ALGORITHMS & POWER CALCULATION.
Innovations: to take the 16PF5 test 3 times.
Why your brain distorts!

PAPER Investigation and application of Personalizing Recommender Systems ....

www.ijana.in/papers/V6I2-2.pdf
 Investigation and application of Personalizing Recommender Systems based on ALIDATA DISCOVERY 

abstract:
To aid in the decision-making process, recommender systems use the available data on the items themselves.
Personalized recommender systems subsequently use this input data, and convert it to an output in the form of ordered lists or scores of items in which a user might be interested. These lists or scores are the final result the user will be presented with, and their goal is to assist the user in the decision-making process. The application of recommender systems outlined was just a small introduction to the possibilities of the extension. Recommender systems became essential in an information- and decision-overloaded world. They changed the way users make decisions, and helped their creators to increase revenue at the same time. 

Please remember:
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)
http://onlinedatingsoundbarrier.blogspot.com.ar/2014/10/proceedings-of-8th-acm-conference-on.html
Please read:
Twitter teams with IBM for business analytics http://onlinedatingsoundbarrier.blogspot.com.ar/2014/10/twitter-teams-with-ibm-for-business.html
 What comes after the Social Networking wave?
The Next Big Investment Opportunity on the Internet will be .... Personalization!
Personality Based Recommender Systems and Strict Personality Based Compatibility Matching Engines for serious Online Dating with the normative 16PF5 personality test.
http://onlinedatingsoundbarrier.blogspot.com.ar/2014/11/paper-recland-recommender-system-for.html
If you want to be first in the "personalization arena" == Personality Based Recommender Systems, you should understand HOW TO INNOVATE in the ............ Online Dating Industry first of all!   

Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges

PAPER Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges.

http://mmc.tudelft.nl/content/collaborative-filtering-beyond-user-item-matrix-survey-state-art-and-future-challenges

Over the past two decades, a large amount of research effort has been devoted to developing algorithms that generate recommendations. The resulting research progress has established the importance of the user-item (U-I) matrix, which encodes the individual preferences of users for items in a collection, for recommender systems. The U-I matrix provides the basis for collaborative filtering (CF) techniques, the dominant framework for recommender systems. Currently, new recommendation scenarios are emerging that offer promising new information that goes beyond the U-I matrix. This information can be divided into two categories related to its source: rich side information concerning users and items, and interaction information associated with the interplay of users and items. In this survey, we summarize and analyze recommendation scenarios involving information sources and the CF algorithms that have been recently developed to address them. We provide a comprehensive introduction to a large body of research, more than 200 key references, with the aim of supporting the further development of recommender systems exploiting information beyond the U-I matrix. On the basis of this material, we identify and discuss what we see as the central challenges lying ahead for recommender system technology, both in terms of extensions of existing techniques as well as of the integration of techniques and technologies drawn from other research areas.

---------------------------

Please remember:
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)
If you want to be first in the "personalization arena" == Personality Based Recommender Systems, you should understand HOW TO INNOVATE in the ................ Online Dating Industry first of all!  
 
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!
but no one is scientifically proven!

The only way to revolutionize the Online Dating Industry is using the 16PF5 normative personality test, available in different languages to assess personality of members, or a proprietary test with exactly the same traits of the 16PF5 and expressing compatibility with eight decimals (needs a quantized pattern comparison method, part of pattern recognition by cross-correlation, to calculate similarity between prospective mates.)
High precision in matching algorithms is precisely the key to open the door and leave the infancy of compatibility testing.
It is all about achieving the eighth decimal!
With 8 decimals, you have more precision than any person could achieve by searching on one's own, but the only way to achieve the eighth decimal is using analysis and correlationwith quantized patterns.
Without offering the NORMATIVE16PF5 (or similar test measuring exactly the 16 personality factors) for serious dating, it will be impossible to innovate and revolutionize the Online Dating Industry All other proposals are NOISE and perform as placebo. 

LIFEPROJECT METHOD: 100X better than eHarmony, not 100% better, 100 times better!

http://es.scribd.com/doc/223672375/LPMvEHARMONY-pdf 
  
What comes after the Social Networking wave?
The Next Big Investment Opportunity on the Internet will be .... Personalization!
Personality Based Recommender Systems and Strict Personality Based Compatibility Matching Engines for serious Online Dating with the normative 16PF5 personality test. 

WOOF: user defined news recommendation system

http://scottshi.com/trunk/file/woof.pdf

7.1.1 Jaccard Similarity

7.1.2 Cosine Similarity 

............................

Please remember:
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)

http://onlinedatingsoundbarrier.blogspot.com.ar/2014/09/paper-improved-network-based.html
If you want to be first in the "personalization arena" == Personality Based Recommender Systems, you should understand HOW TO INNOVATE in the ................ Online Dating Industry first of all!  
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!
but no one is scientifically proven!

The only way to revolutionize the Online Dating Industry is using the 16PF5 normative personality test, available in different languages to assess personality of members, or a proprietary test with exactly the same traits of the 16PF5 and expressing compatibility with eight decimals (needs a quantized pattern comparison method, part of pattern recognition by cross-correlation, to calculate similarity between prospective mates.)
High precision in matching algorithms is precisely the key to open the door and leave the infancy of compatibility testing.
It is all about achieving the eighth decimal!
With 8 decimals, you have more precision than any person could achieve by searching on one's own, but the only way to achieve the eighth decimal is using analysis and correlationwith quantized patterns.
Without offering the NORMATIVE16PF5 (or similar test measuring exactly the 16 personality factors) for serious dating, it will be impossible to innovate and revolutionize the Online Dating Industry All other proposals are NOISE and perform as placebo.

LIFEPROJECT METHOD: 100X better than eHarmony, not 100% better, 100 times better!

http://es.scribd.com/doc/223672375/LPMvEHARMONY-pdf 

Do you want to innovate in the Online Dating Industry?
Read: The 8 tips to innovate in the Online Dating Industry 2014!  
 
What comes after the Social Networking wave?
The Next Big Investment Opportunity on the Internet will be .... Personalization!
Personality Based Recommender Systems and Strict Personality Based Compatibility Matching Engines for serious Online Dating with the normative 16PF5 personality test.