ACM Recommender Systems 2012 Conference
Dublin, Ireland 9th - 13th September 2012
"The ACM Recommender System conference is the premier international forum for the presentation of new research results, systems and techniques in the broad field of recommender systems.
Recommendation is a particular form of information filtering, that exploits past behaviours and user similarities to generate a list of information items that is personally tailored to an end-user's preferences. The sixth conference in this series, RecSys 2012, will bring together researchers and practitioners from academia and industry to present the latest results and identify new trends and challenges in providing recommendation components in a range of innovative application contexts. As RecSys brings together the main international research groups working on recommender systems, along with many of the world's leading e-commerce companies, in the last number of years, it has become the most important annual conference for the presentation and discussion of recommender system research."
What comes after Social Networking?
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.
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 18.104.22.168.22.214.171.124.126.96.36.199.188.8.131.52 is 92.55033557% +/- 0.00000001% similar to the pattern 184.108.40.206.220.127.116.11.18.104.22.168.22.214.171.124
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