Tuesday, December 23, 2014
PAPER Collaborative filtering for people-to-people recommendation in online dating: data analysis and user trial
A common perception is that online dating systems “match” people on the basis of profiles containing demographic and psychographic information and/or user interests. In contrast, product recommender systems are typically based on collaborative filtering, suggesting purchases not based on “content” but on the purchases of “similar” users. In this paper, we study collaborative filtering for people-to-people recommendation in online dating, comparing this approach to a baseline profile matching method. Initial data analysis highlights the problem of over-recommending popular users, a standard problem for collaborative filtering applied to product recommendation, but more acute in people-to-people recommendation. We address this problem with a two-stage recommender process that employs a Decision Tree derived from interactions data as a “critic” to re-rank candidates generated by collaborative filtering. Our baseline profile matching method dynamically chooses, for each user, attributes that contribute most significantly to successful interactions with candidates having the best matching attribute value. The key evaluation metric is success rate improvement, the increase in the chance of a user having a successful interaction when acting on recommendations. Our methods were first evaluated on historical data from a large online dating site and then trialled live over a 9 week period providing recommendations via e-mail to a large number of users. The trial confirmed the consistency of the analysis on historical data and the ability of our collaborative filtering method to generate suitable candidates over an extended period. Moreover, the collaborative filtering method gives a higher success rate improvement than profile matching.
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. They need to calculate personality similarity between users but there are different formulas to calculate similarity.
Similarity is a word that has different meanings for different persons or companies, it exactly depends on how mathematically is defined. In case you had not noticed, recommender systems are morphing to .......... compatibility matching engines, as the same used in the Online Dating Industry since years, with low success rates until now because they mostly use the BIG 5 to assess personality and the Pearson correlation coefficient to calculate similarity.
The BIG 5 (Big Five) normative personality test is obsolete. The HEXACO (a.k.a. Big Six) is another oversimplification. Online Dating sites have very big databases, in the range of 20,000,000 (twenty million) profiles, so the BIG 5 model or the HEXACO model are not enough for predictive purposes. That is why I suggest the 16PF5 test instead and another method to calculate similarity. I calculate similarity in personality patterns with (a proprietary) pattern recognition by correlation method. It takes into account the score and the trend to score of any pattern. Also it takes into account women under hormonal treatment because several studies showed contraceptive pills users make different mate choices, on average, compared to non-users. "Only short-term but not long-term partner preferences tend to vary with the menstrual cycle".
If you want to be first in the "personalization arena" == Personality Based Recommender Systems, you should understand the ............ Online Dating Industry first of all!
Please see: "How to calculate personality similarity between users"
Short answer: the key is the ENSEMBLE!
(the whole set of different valid possibilities)
Worldwide there are over 5,000 online dating sites, no one uses the 16PF5, no one is scientifically proven yet, and no one can show you compatibility distribution curves, i.e. if you are a man seeking women, to show how compatible you are with a 20,000,000 women database, and to select a bunch of 100 women from 20,000,000 women database.
Please read also
An exercise of similarity.
How LIFEPROJECT METHOD calculates similarity.
STRICT PERSONALITY SIMILARITY by LIFEPROJECT METHOD.
Personality Distribution Curves using the NORMATIVE 16PF5.
ALGORITHMS & POWER CALCULATION.
Innovations: to take the 16PF5 test 3 times.
Why your brain distorts!