PAPER Measuring similarity between user profile and library book.
In the development of recommender system either the content or collaborative filtering is necessary. To filter the records it is required to measure the similarity between profile of user and items present in the dataset. This experiment is performed on the dataset containing 978 books related to computer science field and 7 users. Similarity between profile of user and contents of book is measured using Euclidean, Manhattan, Minkowski, Cosine distances. The results are evaluated and compared. This work is useful in the development of library recommender system.
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 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The BIG 5 (Big Five) normative personality test is obsolete.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)
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!