Tuesday, March 8, 2016
PAPER A Scalable People-to-People Hybrid Reciprocal Recommender Using Hidden Markov Models
Paper presented at MLRec 2016
2nd International Workshop on Machine Learning Methods for Recommender Systems
In conjunction with 16th SIAM International Conference on Data Mining (SDM 2016) May, 2016, Miami, Florida, USA
A people-to-people content-based reciprocal recommender using hidden markov models
new PAPER: A New Hybrid Popular Model for Personalized Tag Recommendation
People-to-People Reciprocal Recommenders
PAPER A Deployed People-to-People Recommender System in Online Dating
PAPER Collaborative filtering for people-to-people recommendation in online dating: data analysis and user trial
PAPER Top-N Recommendation with Novel Rank Approximation http://onlinedatingsoundbarrier.blogspot.com.ar/2016/03/paper-top-n-recommendation-with-novel.html
PAPER Similarity Scores Evaluation in Social Networking Sites
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.
All other proposals are NOISE and perform as placebo.