PAPER: Development of a group recommender application in a Social Network

Abstract
http://www.sciencedirect.com/science/article/pii/S095070511400197X
In today's society, recommendations are becoming increasingly important. With the advent of the Social Web and the growing popularity of Social Networks, where users explicitly provide personal information and interact with others and the system, it is becoming clear that the key for the success of recommendations is to develop new strategies which focus on social recommendations leveraged by these new sources of knowledge. In our work, we focus on group recommender systems. These systems traditionally suffer from a number of shortcomings that hamper their effectiveness. In this paper we continue our research, that focuses on improving the overall quality of group recommendations through the addition of social knowledge to existing recommendation strategies. To do so, we use the information stored in Social Networks to elicit social factors following two approaches: the cognitive modeling approach, that studies how people's way of thinking predisposes their actions; and the social approach, that studies how people's relationships predispose their actions. We show the value of using models of social cognition extracted from Social Networks in group recommender systems through the instantiation of our model into a real-life Facebook movie recommender application.

The authors used first an adaptation of the Thomas-Kilmann Conflict Mode Instrument (TKI). The TKI test consists of 30 different situations with two possible answers. Depending on the answers, a score is assigned for 5 existing personality modes organized according to two dimensions: assertiveness and cooperativeness. Then replaced that test with a movie metaphor as an alternative personality test.


Other proposal like the above one is PersonalityML.
Personality markup language (PersonalityML) has the aim of standardize and help to disseminate and share the use of users’ personality information across applications that take human psychological aspects into account in the computer decision making process.
http://personalityresearch.ufs.br/en/products/softwares/personalityml
http://onlinedatingsoundbarrier.blogspot.com.ar/2012/12/about-new-markup-language-called.html

The Big Five (Big 5, FFI, FFM, OCEAN model) normative personality test is obsolete. The HEXACO (a.k.a. Big Six) is another oversimplification. Online Dating sites have very big databases, in the range of 20,000,000 (twenty million) profiles, so the Big Five model or the HEXACO model are not enough for predictive purposes. That is why I suggest the 16PF5 test instead and another method to calculate similarity.

and

PAPER: How do personality traits affect communication among users in online social networks?
http://onlinedatingsoundbarrier.blogspot.com.ar/2014/03/paper-how-do-personality-traits-affect.html
RecSys 2010 Conference was full of papers about Personality based recommender systems, but this RecSys 2011 Conference has only one outstanding paper:
Rong Hu and Pearl Pu, 2011 "Enhancing Collaborative Filtering Systems with Personality Information"
http://onlinedatingsoundbarrier.blogspot.com.ar/2011/09/best-paper-recsys-2011-conference.html
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 HOW TO INNOVATE in the ............ Online Dating Industry first of all!