Alternating Least Squares (ALS) is popular method to compute matrix factorization in the parallel way. However, due to the time complexity in predicting user’s preference, ALS is not scalable to large-scale datasets. In this paper, we propose a similar user index-based parallel matrix factorization approach. Since the group of similar users is indexed in advance, there is no need to compute similarities between all users in datasets. Furthermore, the size of a matrix is reduced because the matrix is only composed of indexed user’s ratings and items. The current advanced cloud computing including Hadoop, MapReduce and Amazon EC2 are employed to implement the proposed approaches. We empirically show that the use of similar user index resolves the scalable issue of ALS and improves the performance of large scale recommender systems in distributed computing environment.
Please see also:
PAPER "A new similarity measure using Bhattacharyya coefficient
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normative personality test is obsolete.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.
What comes after the Social Networking wave?
The Next Big Investment Opportunity on the Internet will be .... Personalization!
Personality Based Recommender Systems and Strict Personality Based Compatibility Matching Engines for serious Online Dating with the normative 16PF5 personality test.