PAPER Improving Performance of Movie Recommendation in Collaborative Filtering Systems

http://www.aicit.org/RNIS/ppl/RNIS270PPL.pdf

Abstract
Collaborative filtering has been most widely used in commercial sites to recommend items based on the history of user preferences for items. The idea behind this method is to find similar users whose ratings for items are incorporated to make recommendation. Hence, similarity calculation is most critical in recommendation performance. For movie recommendation, this paper enhances performance of previous similarity measures by incorporating information on genre difference as well as the number of movies co-rated by two users. Extensive experiment results are provided to demonstrate far enhanced performance improvement for several popular classic CF methods.
Keywords: Similarity measure, Web personalization, Collaborative filtering, Recommender system

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The BIG 5 (Big Five) normative personality test is obsolete.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".
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 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!