Thursday, September 1, 2016
PAPER: The impact of consumer preferences on the accuracy of collaborative filtering recommender systems
The main disadvantage with recommendation engines based on collaborative filtering is when users instead of providing their personal preference try to guess the global preference and they introduce bias in the recommendation algorithm.
PAPER "A New Similarity Measure Based on Mean Measure of Divergence for Collaborative Filtering in Sparse Environment"
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)
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.
High precision in matching algorithms is precisely the key to open the door and leave the infancy of compatibility testing.
Without offering the NORMATIVE 16PF5 (or similar test measuring exactly the 16 personality factors) for serious dating, it will be impossible to innovate and revolutionize the Online Dating Industry. The Online Dating Industry does not need a 10% improvement, a 50% improvement or a 100% improvement. It does need "a 100 times better improvement"
All other proposals are NOISE and perform as placebo.