NEW paper: "Recommender System"

http://ijcsmc.com/docs/papers/July2014/V3I7KJ10.pdf
Abstract-  One of the major data mining applications is Recommender System. It is the intelligent system that basically investigate the dataset present in existing system and based on which it will give some suggestions to the user  regarding  further  process.  These  recommender  systems  are  generally  application  specific  and  work  on certain parameters. In this present work we define a hybrid recommender system for the movies ranking. A movie based recommender system suggests the user about the movie that he should rank after performing the intelligent analysis. In this present work, we are defining three dimensions to get the concept of hybridization. This kind of dataset  having two main  dimensions  called  users,  movies  and  the  relationship. The  first level  analysis  will  be based on user side where the content based weighted similarity analysis will be performed. Once the similar users will be identified, the next work is performed on movie side. The similar movies based on different aspects are identified  using  content  based  weighted  analysis.  At  the  third  level,  the  similarity  analysis  between  the relationships  is  identified  using  collaborative  analysis.  To  perform  the  collaborative  analysis,  correlation coefficient  will  be  used.  Once  these  three  level  analysis  will  be  completed,  the  next  work  is  to conclude  the relationship using weighted approach. The weightage will be applied all three methods and obtain the analysis.
Another improvement  here defined  is the analysis  under the temporal  vector.  It  means,  instead of  analysis  on whole dataset, the dataset in same time domain will be considered only. The work will be implemented in Matlab environment.

Please read:
Some new and fresh PAPERS from The 37th Annual ACM SIGIR 2014 CONFERENCE
http://onlinedatingsoundbarrier.blogspot.com.ar/2014/07/some-new-and-fresh-papers-from-37th.html

Please remember:
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) 
http://onlinedatingsoundbarrier.blogspot.com.ar/2014/07/paper-clustering-based-online-learning.html
http://onlinedatingsoundbarrier.blogspot.com.ar/2014/06/more-new-and-fresh-papers-about.html
 
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.
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!