PAPER: An Improved Collaborative Recommendation System by Integration of Social Tagging Data

http://link.springer.com/chapter/10.1007/978-3-319-14379-8_7#page-1

Abstract
Recently a lot of research efforts have been spent on building recommender systems by utilizing the abundant online social network data. In this study, we intend to enhance the recommendation accuracy via integrating social networking information with the traditional recommendation algorithms. To achieve this goal, we first propose a new user similarity metric that not only considers tagging activities of users, but also incorporates their social relationships such as friendship and membership, in measuring the closeness of two users. Then we define a new item prediction method which makes use of both user-to-user similarity and item-to-item similarity. Experimental outcomes on Last.fm data produce the positive results that show the accuracy of our proposed approach.


Please see also:
Constructing & Evaluating Context-Aware Recommender System in a case study with webshop carts and AB-testing
Master of Science Thesis in Algorithm, Language and Logic
http://publications.lib.chalmers.se/records/fulltext/212556/212556.pdf
PAPER VizRec: A Two-Stage Recommender System for Personalized Visualizations 
http://onlinedatingsoundbarrier.blogspot.com.ar/2015/02/paper-vizrec-two-stage-recommender.html


Behavioural recommender systems or other system that learns your preferences are useless for serious online dating purposes and 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.  
http://onlinedatingsoundbarrier.blogspot.com.ar/2015/02/article-how-dating-companies-are-having.html

If you want to be first in the "personalization arena" == Personality Based Recommender Systems, you should understand HOW TO INNOVATE in the ................ Online Dating Industry first of all! 
WorldWide, there are over 5,000 -five thousand- online dating sites
but no one is using the 16PF5 (or similar) to assess personality of its members!
but no one calculates similarity with a quantized pattern comparison method!
but no one can show Compatibility Distribution Curves to each and every of its members!
but no one is scientifically proven!

LIFEPROJECT METHOD: 100X better than eHarmony, not 100% better, 100 times better!

http://es.scribd.com/doc/223672375/LPMvEHARMONY-pdf 
  
What comes after the Social Networking wave?
The Next Big Investment Opportunity on the Internet will be .... Personalization!
Personality Based Recommender Systems and Strict Personality Based Compatibility Matching Engines for serious Online Dating with the normative 16PF5 personality test.