ACM Recommender Systems 2012 series.

Presentations:

"How Users Evaluate Each Other in Social Media"



Recommender systems (a.k.a recommendation engines) can be based on:

- past actions (as the formerly Beacon at Facebook)

- a pattern of personal preferences (by collaborative filtering, as the actual one at Facebook) The main disadvantage with recommendation engines based on collaborative filtering is when users instead of providing their personal preference try to guess the global preference and they introduce bias in the recommendation algorithm.

- personality traits of users.

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.
Have you seen they need to calculate personality similarity between users?
Have you seen there are different formulas to calculate similarity?
In case you did not notice, recommender systems are morphing to .......... compatibility matching engines!!!
They mostly use the Big5 to assess personality and the Pearson correlation coefficient to calculate similarity.
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What comes after Social Networking?
My bet: 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. The market remains enormous!!