Wednesday, July 9, 2014

PAPER: Clustering Based Online Learning in Recommender Systems: A Bandit Approach

PAPER: Clustering Based Online Learning in Recommender Systems: A Bandit Approach
" ... we  develop  an  algorithm that  does  exploration  and  exploitation  in  the  context  space  and  the item  space  separately,  and  develop  an  algorithm  that  combines clustering  of  the  items  with  information  aggregation  in  the  context space. "

Please see also:
More new and fresh papers about recommender systems

Some new and fresh PAPERS from The 37th Annual ACM SIGIR 2014 CONFERENCE

 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. 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 Five to assess personality and the Pearson correlation coefficient to calculate similarity.
The Big Five (Big 5, FFI, FFM, OCEAN model) normative personality test is obsolete.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 Five 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".
[Also some Psychologists began to encourage the use of other tests for the Online Dating Industry, like:
California Psychological Inventory (CPI)
The Millon Index of Personality Styles-Revised (MIPS Revised)

but my best recommendation is, of course, the 16PF5 normative personality test.
Nor the CPI nor the MIPS can outperform the 16PF5.]

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.

If you want to be first in the "personalization arena" == Personality Based Recommender Systems, you should understand HOW TO INNOVATE in the ............ Online Dating Industry first of all! 

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