COMMPER 2012 is a workshop in conjunction with The European Conference on Machine Learning and Practice of Knowledge Discovery in Databases (ECML PKDD), which will take place in Bristol, UK from September 24th to 28th, 2012.
"People recommenders, the second main topic of this workshop, deal with the problem of finding meaningful relationships among people or organizations. In online social networks, relationships can be friends on Facebook, professional contacts on LinkedIn, dates on an online dating site, jobs or workers on employment websites, or people to follow on Twitter. The nature of these domains makes people-to-people recommender systems to be significantly different from traditional item-to-people recommenders. One basic difference in the people recommender domain is the benefit or requirement of reciprocal relationships. Another difference between these domains is that people recommenders are likely to have rich user profiles available. The goal of this workshop is to build a community around people recommenders and instigate discussion about this emerging area of research for recommender systems. With this workshop, we want to reach out to research done in both academia and industry. "
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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.
WorldWide, there are over 5,000 -five thousand- online dating sites
?but no one is using the 16PF5 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!
I had reviewed over 55 compatibility matching engines intended for serious dating since 2003, when I had discovered "the online dating sound barrier" problem.
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!
The only way to achieve that 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.
The ensemble of the 16PF5 is: 10E16, big number as All World Population is nearly 7.0 * 10E9
- expressing compatibility with eight decimals, like The pattern 6.7.6.8.9.6.7.7.8.7.2.5.8.7.3.4 is 92.55033557% +/- 0.00000001% similar to the pattern 7.7.6.8.8.7.6.5.8.7.4.5.7.7.3.4
Using a quantized pattern comparison method (part of pattern recognition by cross-correlation) to calculate similarity between prospective mates.
That is the only way to revolutionize the Online Dating Industry.
All other proposals are .............. NOISE
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