SybilSort algorithm - a friend request decision tracking recommender system in online social networks

2021 ◽  
Author(s):  
Poornima Nedunchezhian ◽  
Murugan Mahalingam
Keyword(s):  
Social Networks ◽  
Recommender System ◽  
Online Social Networks

SOS: A multimedia recommender System for Online Social networks

2019 ◽  
Vol 93 ◽  
pp. 914-923 ◽  
Author(s):  
Flora Amato ◽  
Vincenzo Moscato ◽  
Antonio Picariello ◽  
Francesco Piccialli
Keyword(s):  
Social Networks ◽  
Recommender System ◽  
Online Social Networks

A Social Media Recommender System

2018 ◽  
Vol 9 (1) ◽  
pp. 36-50 ◽  
Author(s):  
Giancarlo Sperlì ◽  
Flora Amato ◽  
Fabio Mercorio ◽  
Mario Mezzanzanica ◽  
Vincenzo Moscato ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Recommender System ◽  
Online Social Networks ◽  
Online Social Network ◽  
Multimedia Contents ◽  
Big Data Applications ◽  
The Social ◽  
And Behavior ◽  
Content Information

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.

A Social Media Recommender System

2021 ◽  
pp. 541-556
Author(s):  
Giancarlo Sperlì ◽  
Flora Amato ◽  
Fabio Mercorio ◽  
Mario Mezzanzanica ◽  
Vincenzo Moscato ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Recommender System ◽  
Online Social Networks ◽  
Online Social Network ◽  
Multimedia Contents ◽  
Big Data Applications ◽  
The Social ◽  
And Behavior ◽  
Content Information

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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