Recommender systems (a.k.a recommendation engines) can be based on:
- past actions (as the formely 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)
Have you seen Personality Based Recommender Systems need to calculate personality similarity between users?
Have you seen there are different formulas to calculate similarity?
Recommender systems are morphing to .......... compatibility matching engines!!!
That is nothing new, nothing innovative. Online Dating Sites like eHarmony, Parship, Be2, MeeticAffinity and others had been calculating personality similarity between prospective users since several years ago with low successful rates, with a low effectiveness/efficiency level of their matching algorithms (less than 10%) because they use the normative Big5 or ipsative proprietary models instead -like Chemistry or PerfectMatch- to measure personality traits.
No one is using the 16PF5 to assess personality of members.
No one calculates similarity with a quantized pattern comparison method.
No one can show Compatibility Distribution Curves to each and every of its members.
Please do not think I am rude or not polite, or do not think I am hammering the "personality similarity" concept in your head, but
Can you see where the Online Dating Industry for serious daters needs to go?
There is only one road, the road of:
the 16PF5, 15FQ+ or similar to assess personality of members
and
quantized pattern comparison method to calculate similarity.
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