e-learning recommender system for learners in online social networks through association retrieval

Author(s):  
Pragya Dwivedi ◽  
Kamal K. Bharadwaj
2019 ◽  
Vol 93 ◽  
pp. 914-923 ◽  
Author(s):  
Flora Amato ◽  
Vincenzo Moscato ◽  
Antonio Picariello ◽  
Francesco Piccialli

Author(s):  
Andrew Laghos

The purpose of this chapter is to investigate Multimedia Social Networks and e-Learning, and the relevant research in these areas. Multimedia Social Networks in e-Learning is an important and evolving study area, since an understanding of the technologies involved as well as an understanding of how the students communicate in online social networks are necessary in order to accurately analyze them. The chapter begins by introducing Multimedia Social Networks and Online Communities. Following this, the key players of e-Learning in Multimedia Social Networks are presented, including a discussion of the different roles that the students take. Furthermore, Social Interaction research is presented concentrating on such important areas as factors that influence social interaction, peer support, student-centered learning, collaboration, and the effect of interaction on learning. The last section of the chapter deals with the various methods and frameworks for analyzing multimedia social networks in e-Learning communities.


2011 ◽  
Vol 5 (8) ◽  
pp. 1147-1154 ◽  
Author(s):  
J.J.P.C. Rodrigues ◽  
L. Zhou ◽  
F.M.R. Sabino

2017 ◽  
Vol 405 ◽  
pp. 107-122 ◽  
Author(s):  
Pasquale De Meo ◽  
Fabrizio Messina ◽  
Domenico Rosaci ◽  
Giuseppe M.L. Sarné

Author(s):  
Giancarlo Sperlì ◽  
Flora Amato ◽  
Fabio Mercorio ◽  
Mario Mezzanzanica ◽  
Vincenzo Moscato ◽  
...  

Social media recommendation differs from traditional recommendation approaches as it needs considering not only the content information and users' similarities, but also users' social relationships and behavior within an online social network as well. In this article, a recommender system – designed for big data applications – is used for providing useful recommendations in online social networks. The proposed technique represents a collaborative and user-centered approach that exploits the interactions among users and generated multimedia contents in one or more social networks in a novel and effective way. The experiments performed on data collected from several online social networks show the feasibility of the approach towards the social media recommendation problem.


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