Friday, February 28, 2014
PAPER Predicting User Personality by Mining Social Interactions in Facebook
Adaptive applications may benefit from having models of users' personality to adapt their behavior accordingly. There is a wide variety of domains in which this can be useful, i.e., assistive technologies, e-learning, e-commerce, health care or recommender systems, among others. The most commonly used procedure to obtain the user personality consists of asking the user to fill in questionnaires. However, on one hand, it would be desirable to obtain the user personality as unobtrusively as possible, yet without compromising the reliability of the model built. On the other hand, our hypothesis is that users with similar personality are expected to show common behavioral patterns when interacting through virtual social networks, and that these patterns can be mined in order to predict the tendency of a user personality. With the goal of inferring personality from the analysis of user interactions within social networks, we have developed TP2010, a Facebook application. It has been used to collect information about the personality traits of more than 20,000 users, along with their interactions within Facebook. Based on all the collected data, automatic classifiers were trained by using different machine-learning techniques, with the purpose of looking for interaction patterns that provide information about the users' personality traits. These classifiers are able to predict user personality starting from parameters related to user interactions, such as the number of friends or the number of wall posts. The results show that the classifiers have a high level of accuracy, making the proposed approach a reliable method for predicting the user personality.
The TP2010 implements the ZKPQ-50-cc personality test. The set of questions from ZKPQ-50-cc questionnaire plus the ten infrequency questions have been named ZKPQ-60.
The Zuckerman-Kuhlman Personality Questionnaire (ZKPQ)
"The alternative five model of personality is based on the claim that the structure of human personality traits is best explained by five broad factors called impulsive sensation seeking (ImpSS), neuroticism–anxiety (N-Anx), aggression–hostility (Agg-Host), sociability (Sy), and activity (Act). The model was developed by Marvin Zuckerman and colleagues as a rival to the well-known Five factor model of personality traits and is based on the assumption that "basic" personality traits are those with a strong biological-evolutionary basis. One of the salient differences between these two models is that the alternative five model lacks any equivalent to the dimension called openness to experience in the five factor model. ....
A study comparing Zuckerman's model with the Five Factor model found that openness to experience did appear to be a separate personality dimension from the other traits in the five factor model and the alternative five."
Please remember: Big Five (a.k.a. Five Factor, BIG 5 or OCEAN model) normative test had been proven/revealed as an incomplete and incorrect model to assess/measure personality of persons.
(For Online Dating Sites like eHarmony and similars; OFFERING COMPATIBILITY MATCHING METHODS BASED ON PERSONALITY SIMILARITY)
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.
paper: Personality and Patterns of Facebook Usage.
PAPER "Private traits and attributes are predictable from digital records of human behavior"
"The Twitter Me Is Not The Real Me"
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 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 Five (a.k.a. Five Factor, BIG 5 or OCEAN model) to assess personality and the Pearson correlation coefficient to calculate similarity.
The Big Five normative personality test is obsolete, it has been proven as an incomplete and incorrect model of personality.
The HEXACO (a.k.a. Big Six) is another oversimplification.
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 test instead.
The same applies for Personality Based Recommender Systems. Please see: "How to calculate personality similarity between users" Short answer: the key is the ENSEMBLE!
(the whole set of different valid possibilities)
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 or 15FQ+ test or similar to assess personality traits and a new method to calculate similarity between quantized patterns.