Collaborative Filtering: User Similarity in Slope One Algorithm

http://www.jofcis.com/publishedpapers/2014_10_24_10413_10422.pdf

Abstract
Slope One algorithm was kind of item-based collaborative filtering. As a recently proposed algorithm, Slope One has several desirable properties such as simple, being updatable on the fly, efficient to compute. Even though the Slope One algorithm was widely used on large data sets, it performs not so well in terms of accuracy. The reason is that the Slope One algorithm ignored an important factor: similarity between users. Using MovieLens data, we find that the Slope One algorithm is only applicable to similar user groups, the noise which the users have the opposite profile leads to the low accuracy. Based on our findings, we proposed an improved weighted Slope One algorithm which is more accurate. In the end,
the results of the experiments on MovieLens data sets confirmed the effectiveness of our methods.
Keywords : Collaborative Filtering; Recommender System; Slope One; Similarity.

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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 for 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The BIG 5 (Big Five) normative personality test is obsolete.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".
http://onlinedatingsoundbarrier.blogspot.com.ar/2014/12/paper-collaborative-filtering-for.html
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)
http://onlinedatingsoundbarrier.blogspot.com.ar/2013/03/how-to-calculate-personality-similarity.html


Worldwide there are over 5,000 online dating sites, no one uses the 16PF petsonality test, 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.

Other posts:
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!