new paper presented at Association for the Advancement of Artificial Intelligence (AAAI)
Pensacola Beach, Florida. May 21–23, 2014.
PAPER: "How to Improve Multi-Agent Recommendations Using Data from Social Networks?"
User profiles have an important role in multi-agent recommender systems. The information stored in them improves the system's generated recommendations. Multi-agent recommender systems learn from previous recommendations to update users' profiles and improving next recommendations according to the user feedback. However, when the user does not evaluate the recommendations the system may deliver poor recommendations in the future. This paper presents a mechanism that explores user information from social networks to update the user profile and to generate implicit evaluations on behalf of the user. The mechanism was validated with travel packages recommendations and some preliminary results illustrate how user information gathered from social networks may help to improve recommendations in multi-agent recommender systems.
Science and Information Conference 2014 August 27-29, 2014 London, UK
FULL slides from Tutorial: Recommender Systems
AAAI 2013 Workshop on Intelligent Techniques For Web Personalization and Recommender Systems
Personality based recommenders are the next generation of recommender systems.
Do you know that 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The Big Five (Big 5, FFI, FFM, OCEAN model) normative personality test is obsolete.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.]
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!
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.