FEATURED PAPER: Improving User Profile with Personality Traits

Improving User Profile with Personality Traits Predicted from Social Media Content

ABSTRACT
Existing studies indicate that there exists strong correlation between personality and personal preference, thus personality could potentially be used to build more personalized recommender system. Personality traits are mainly measured by psychological questionnaires, and it is hard to obtain personality traits of large amount of users in real-world scenes.
In this paper, we propose a new approach to automatically identify personality traits with Social Media contents in Chinese language environments. Social Media content features were extracted from 1766 Sina micro blog users, and the predicting model is trained with machine learning algorithms.
The experimental results demonstrate that users' personality traits could be predicted from Social Media contents with acceptable Pearson Correlation, which makes it possible to develop user profiles for recommender system. In future, user profiles with predicted personality traits would be used to enhance the performance of existing personalized recommendation systems.


The above paper is from RecSys '13 Proceedings of the 7th ACM conference on Recommender systems.

Some key points and my thoughts:

The HEXACO normative test will replace the obsolete Big Five Model of personality.

"individuals with similar personality traits prefer to similar types of music " seems to be wrong.
"Value similarity is the missing link in explaining the musical bonding phenomenon [and not personality similarity], which seems to hold for Western and non-Western samples and in experimental and natural settings. " extracted from paper "How Shared Preferences in Music Create Bonds Between People: Values as the Missing Link" 2011

Profiling by music preferences to assess personality
video preferences to assess personality
color preferences to assess personality
bookmarks preferences to assess personality
handwriting analysis to assess personality
purchases and buying trends from credit cards to assess personality
facial features to assess personality
and other methods like them add distortion to the measurement.

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 since years, with low success rates!!! because they mostly use the Big5 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 have very big databases, in the range of 20,000,000 (twenty million) profiles, so the BIG5 model or the HEXACO model are not enough for predictive purposes. That is why I suggest the 16PF5 test.


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 correlation with 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.

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.