In recent times we have witnessed the emergence of large online markets with two-sided preferences that are responsible for businesses worth billions of dollars.
Recommendation systems are critical components of such markets. It is to be noted that the matching in such a market depends on the preferences of both sides, consequently, the construction of a recommendation system for such a market calls for consideration of preferences of both sides. The online dating market, and the online freelancer market are examples of markets with two-sided preferences.
Recommendation systems for such markets are fundamentally different from typical rating based product recommendations. We pose this problem as a bipartite ranking problem. There has been extensive research on bipartite ranking algorithms. Typically, generalized linear regression models are popular methods of constructing such ranking on account of their ability to be learned easily from big data, and their computational simplicity on engineering platforms. However, we show that for markets with two sided preferences, one can improve the AUC (Area Under the receiver operator Curve) score by considering separate models for preferences of both the sides and constructing a two layer architecture for ranking. We call this a two-level model algorithm. For both synthetic and real data we show that the two-level model algorithm has a better AUC performance than the direct application of a generalized linear model such as L1 logistic regression or an ensemble method such as random forest algorithm. We provide a theoretical justification of AUC optimality of two-level model and pose a theoretical problem for a more general result.
Please see also:
PAPER Collaborative filtering for people-to-people recommendation in online dating: data analysis and user trial
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)
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 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".
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)
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!