More plague of rubbish recommender systems

More plague of rubbish recommender systems, useless at all for online dating purposes because:
The Online Dating Industry needs innovations but they will come from only one source: the latest discoveries in theories of romantic relationships development with commitment.
I) Several studies showing contraceptive pills users make different mate choices, on average, compared to non-users.
II) People often report partner preferences that are not compatible with their choices in real life.(FORGET Behavioural recommender systems or other system that learns your preferences)
III) Compatibility is all about a high level on personality* similarity* between prospective mates for long term mating with commitment.
*personality measured with a normative test.
*similarity: there are different ways to calculate similarity, it depends on how mathematically is defined.


"Measuring Profile Distance in Online Social Networks"
".... We present a method for comparing user profiles, by measuring the distance between the profiles in metric space"

See How LIFEPROJECT METHOD calculates similarity between quantized patterns using an adapted quantum mechanics math equation. All other methods are RUBBISH, because the ensemble of the 16PF5 is: 10E16, big number as All World Population is nearly 7.0 * 10E9 (estimated OCT 2011)

(7.0 * 10E9) / 10E16 == 7.0 * 10E(-7) or 0.7 * 10E(-6) or 0.7 micro part!

All World Population is less than 0.7 micro part of the 16PF5's ensemble.

other rubbish paper for the online dating industry:
"A Hybrid Content-Collaborative Reciprocal Recommender for Online Dating"
"We present a new recommender system for online dating. Using a large dataset from a major online dating website, we first show that similar people, as defined by a set of personal attributes, like and dislike similar people and are liked and disliked by similar people. This analysis provides the foundation for our reciprocal content-collaborative recommender approach. The content-based part uses selected user profile features and similarity measure to generate a set of similar users. The collaborative filtering part uses the interactions of the similar users, including the people they like/dislike and are liked/disliked by, to produce reciprocal recommendations....."

is the evolution of "CCR - A Content-Collaborative Reciprocal Recommender for Online Dating"