Abstract: Collaborative filtering recommendation algorithm is one of the most successful technologies for building recommender systems. However, a user-based collaborative filtering method has its limits related to similarity and ratings. To avoid those limits, we propose a new item-based collaborative filtering algorithm based on conditional probability and weight adjusting in this paper. At first, any two items are selected to compute the similarity from common user ratings, and only the items with the similarity greater than preset thresholds are chosen as the set of supporting items. Then an integral parameter, that is the frequency of two items present simultaneously, is used to adjusted similarity weights. Finally, a new algorithm combining conditional probability and weight adjusting is proposed to predict ratings. The experimental results show the proposed algorithm is feasibleand effective in practice.
Please see also:
Paper: Recommendation Systems for Markets with Two Sided Preferences
PAPER Collaborative filtering for people-to-people recommendation in online dating: data analysis and user trial
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normative personality test is obsolete.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.
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!