Tuesday, March 31, 2015

PAPER New Recommender Framework: Combining Semantic Similarity Fusion and Bicluster Collaborative Filtering


TB-CA: A hybrid method based on trust and context-aware for recommender system in social networks

A Cluster-Based Similarity Fusion Approach for Scaling-Up Collaborative Filtering Recommender System

Please see also:
PAPER: Activity-Partner Recommendation  

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).
If you want to be first in the "personalization arena" == Personality Based Recommender Systems, you should understand the ............ Online Dating Industry first of all!
The key to long-lasting romance is STRICT PERSONALITY SIMILARITY, but ...
the only way to revolutionize the Online Dating Industry is using the 16PF5 normative personality test, available in different languages to assess personality of members, or a proprietary test with exactly the same traits of the 16PF5 and expressing compatibility with eight decimals (needs a quantized pattern comparison method, part of pattern recognition by cross-correlation, to calculate similarity between prospective mates.)
High precision in matching algorithms is precisely the key to open the door and leave the infancy of compatibility testing.

  The Online Dating Industry does not need any improvement. It does need INNOVATIONS!
Online Dating for serious daters does not need to be more social, it needs to be more effective/efficient. It needs to reduce the false positives problem.

The 8 tips to innovate in the Online Dating Industry!

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