Paper Info
Title
Recommender Systems for Online and Mobile Social Networks: A survey.
Abstract
Recommender Systems (RS) currently represent a fundamental tool in online services, especially with the advent of Online Social Networks (OSN). In this case, users generate huge amounts of contents and they can be quickly overloaded by useless information. At the same time, social media represent an important source of information to characterize contents and users’ interests. RS can exploit this information to further personalize suggestions and improve the recommendation process. In this paper we present a survey of Recommender Systems designed and implemented for Online and Mobile Social Networks, highlighting how the use of social context information improves the recommendation task, and how standard algorithms must be enhanced and optimized to run in a fully distributed environment, as opportunistic networks. We describe advantages and drawbacks of these systems in terms of algorithms, target domains, evaluation metrics and performance evaluations. Eventually, we present some open research challenges in this area.
Year
DOI
Venue
2017
10.1016/j.osnem.2017.10.005
Online Social Networks and Media
Keywords
Field
DocType
Recommender Systems,Online Social Networks,Mobile Social Networks
Open research,Social environment,Recommender system,Standard algorithms,Social media,Social network,Distributed Computing Environment,Computer science,Exploit,Multimedia
Journal
Volume
ISSN
Citations 
3
2468-6964
7
PageRank 
References 
Authors
0.48
125
2
Search Limit
100125
Name
Order
Citations
PageRank
Mattia Giovanni Campana1132.64
Franca Delmastro221119.88