People-to-people reciprocal recommenders are an emerging class of recommender systems. They differ from traditional items-to-people recommenders as they must satisfy the preferences and needs of the two parties involved in the recommendation. In contrast, traditional items-to-people recommenders are one-sided and must satisfy only the preference of the person for whom the recommendation is generated. We review the characteristics and present an overview of existing reciprocal recommenders. To illustrate the various aspects of these recommenders and how reciprocity can be taken into account in building and evaluating such recommenders, we present a case study in online dating. We describe our reciprocal recommender algorithm that combines content-based and collaborative filtering and uses data from both user profiles and user interactions. We also study the differences between the implicit and explicit user preferences and show that implicit preferences, learned from user interactions, are better predictors of successful interactions. We conclude by outlining some future research directions.
PAPER A Deployed People-to-People Recommender System in Online Dating
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. Oh that is exactly ............ guess ............. yes ........ LIFEPROJECT METHOD, ready since 2001!
All other proposals are NOISE and perform as placebo.
Please read: The 8 tips to innovate in the Online Dating Industry!
PAPER "Homogeneity of personal values and personality traits in Facebook social networks"
PAPER Recommender Systems supporting Decision Making through Analysis of User Emotions and Personality
ACM RecSys CrowdRec 2015 Workshop