paper presented at ACM RecSys 2016 The 10th ACM Recommender Systems Conference Boston, MA, USA from Sept 15-19, 2016.
People recommenders have become a rich research area within the broad recommender systems community and social recommender systems in particular. From "people you may know" and "who to follow" widgets, through people introduction at conferences, job recommendations and job-candidate search, to dating partner matchmakers, people recommendations proliferate. This tutorial will present an overview of the people recommender systems domain. We will present the different types and use cases of people recommendations, the special techniques used to recommend people to themselves, key research work, and open challenges.
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PAPER A Scalable People-to-People Hybrid Reciprocal Recommender Using Hidden Markov Models
NEW PAPER: Using Personality for a Cooperation-Recommender-System in Online Social Networks
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.
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.