Wednesday, May 4, 2016

PAPER Improving Recommender Systems’ Performance on Cold-start Users and Controversial Items by a New Similarity Model

Similarity is a word that has different meanings for different persons or companies, it exactly depends on how mathematically is defined.
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.
In case you had not noticed, recommender systems are morphing to compatibility matching engines, as the same used in the Online Dating Industry. 
Which is the RIGHT approach to innovate in the Personality Based Recommender Systems Arena?
The same approach to innovate in the Online Dating Industry == 16PF5 test or similar to assess personality traits and a new method to calculate similarity between quantized patterns.  LIFEPROJECT METHOD 

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