Please see also:
PAPER Improving the Scalability of ALS-based Large Recommender Systems with Similar User Index
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normative personality test is obsolete.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!