Saturday, January 14, 2017
PAPER: A Comparative Study of People-to-People Recommender Algorithms in Hybrid Method
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.
In case you did not notice, recommender systems are morphing to compatibility matching engines, as the same used in the Online Dating Industry for years, with low success rates!!! Because they mostly use the Big Five model to assess personality and the Pearson correlation coefficient to calculate similarity.
Please remember: Personality traits are highly stable in persons over 25 years old to 45 years old.
Online Dating sites OFFERING COMPATIBILITY MATCHING METHODS BASED ON PERSONALITY SIMILARITY 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 normative personality test instead.
The same applies for Personality Based Recommender Systems.
Please see also:
People-to-People Reciprocal Recommenders
A people-to-people content-based reciprocal recommender using hidden markov models
How AI can help you find a date ?
PAPER: Tensor Methods and Recommender Systems
Artificial Intelligence to Help You Meet Your Soulmate ?