PAPER Similarity Scores Evaluation in Social Networking Sites

Abstract

In today's world, social networking sites are becoming increasingly popular. Often we find suggestions for friends, from such social networking sites. These friend suggestions help us identify friends that we may have lost touch with or new friends that we may want to make. At the same time, these friend suggestions may not be that accurate. To recommend a friend, social networking sites collect information about user's social circle and then build a social network based on this information. This network is then used to recommend to a user, the people he might want to befriend. FoF algorithm is one of the traditional techniques used to recommend friends in a social network. Delta-SimRank is an algorithm used to compute the similarity between objects in a network. This algorithm is also applied on a social network to determine the similarity between users. Here, we evaluate Delta-SimRank and FoF algorithm in terms of the friend suggestion provided by them, when applied on a Facebook dataset. It is observed that Delta-SimRank provides a higher precise similarity score because it considers the entire network around a user.


http://link.springer.com/chapter/10.1007/978-81-322-2674-1_57


See also the paper
User Recommendation Based on Network Structure in Social Networks

Abstract

Advances in Web 2.0 technology has led to the popularity of social networking sites. One fundamental task for social networking sites is to recommend appropriate new friends for users. In recent years, network structure has been used for user recommendation. Most existing network structure-based recommendation methods either need to pre-specify the group number and structure type or fail to improve performance. In this paper, we propose a novel network structure-based user recommendation method, called Bayesian nonparametric mixture matrix factorization (BNPM-MF). The BNPM-MF model first employs a Bayesian nonparametric model to automatically determine the group number and the network structure in networks and then applies a matrix factorization method on each structure to user recommendation for improvement. Experiments conducted on a number of real networks demonstrate that the BNPM-MF model is competitive with other state-of-the-art methods.

http://link.springer.com/chapter/10.1007/978-3-319-26555-1_55

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!  
http://onlinedatingsoundbarrier.blogspot.com.ar/2015/11/paper-new-similarity-measure-for-user.html
http://onlinedatingsoundbarrier.blogspot.com.ar/2015/10/paper-recommender-systems-supporting.html http://onlinedatingsoundbarrier.blogspot.com.ar/2015/04/paper-evaluation-of-similarity-functions.html

Similarity is a word that has different meanings for different persons or companies, it exactly depends on how mathematically is defined.
http://onlinedatingsoundbarrier.blogspot.com.ar/2015/04/paper-hybrid-personalized-recommender.html

Please see: "How to calculate personality similarity between users"
Short answer: the key is the ENSEMBLE!
(the whole set of different valid possibilities)
http://onlinedatingsoundbarrier.blogspot.com.ar/2013/03/how-to-calculate-personality-similarity.html

 Please read also
PAPER Evaluation of Similarity Functions 
http://onlinedatingsoundbarrier.blogspot.com.ar/2015/04/paper-evaluation-of-similarity-functions.html
An exercise of similarity.
How LIFEPROJECT METHOD calculates similarity.
STRICT PERSONALITY SIMILARITY by LIFEPROJECT METHOD.
Personality Distribution Curves using the NORMATIVE 16PF5.
ALGORITHMS & POWER CALCULATION.
Innovations: to take the 16PF5 test 3 times.
Why your brain distorts!
Matching Algorithms for the Online Dating Industry (serious daters)  
http://onlinedatingsoundbarrier.blogspot.com.ar/2013/01/matching-algorithms-for-online-dating.html 
http://onlinedatingsoundbarrier.blogspot.com.ar/2015/02/article-how-dating-companies-are-having.html
 
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 8 tips to innovate in the Online Dating Industry!
http://onlinedatingsoundbarrier.blogspot.com.ar/2013/12/the-8-tips-to-innovate-in-online-dating.html  

The next wave of innovation!
http://onlinedatingsoundbarrier.blogspot.com.ar/2013/06/the-next-wave-of-innovation.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.
Without offering the NORMATIVE16PF5 (or similar test measuring exactly the 16 personality factors) for serious dating, it will be impossible to innovate and revolutionize the Online Dating Industry.