scholarly journals YourPrivacyProtector: A Recommender System for Privacy Settings in Social Networks

2013 ◽  
Vol 2 (4) ◽  
pp. 11-25 ◽  
Author(s):  
Kambiz Ghazinour ◽  
Stan Matwin ◽  
Marina Sokolova
Keyword(s):  
Social Networks ◽  
Recommender System ◽  
Privacy Settings

Colour as an Indicator of Vulnerability of Users Within Social Networks

2016 ◽  
Vol 8 (1) ◽  
pp. 56-69
Author(s):  
Amanda Cox ◽  
Yeslam Al-Saggaf ◽  
Kate McLean
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Networking ◽  
User Profile ◽  
Risk Matrix ◽  
Individual User ◽  
Privacy Settings ◽  
Personally Identifiable Information ◽  
The Individual

Social networking users are presented with a plethora of profile and privacy settings; most of which are left defaulted. As a result, there is little understanding of the fields that make up the user profile, the privacy settings available to safeguard the user, and the ramifications of not changing the same. Concerns relating to the unprecedented quantities of Personally Identifiable Information being stored need to be addressed. By employing a risk matrix to a social media profile, a user could be alerted to the potential dangers of the information being contained within the profile. By adapting this tool, the risks to the individual user of a social media profile will be minimised.

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 Usability Evaluation of Facebook's Privacy Features Based on the Perspectives of Experts and Users

2019 ◽  
pp. 1544-1568
Author(s):  
Márcio J. Mantau ◽  
Marcos H. Kimura ◽  
Isabela Gasparini ◽  
Carla D. M. Berkenbrock ◽  
Avanilde Kemczinski
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
User Interaction ◽  
Usability Evaluation ◽  
Second Step ◽  
Web Based ◽  
The Third ◽  
Privacy Settings ◽  
Three Stages

The issue of privacy in social networks is a hot topic today, because of the growing amount of information shared among users, who are connected to social media every moment and by different devices and displays. This chapter presents a usability evaluation of the privacy features of Facebook's social network. The authors carry out an evaluation composed by three approaches, executed in three stages: first by the analysis and inspection of system's features related to privacy, available for both systems (Web-based systems and mobile-based systems, e.g. app). The second step is a heuristic evaluation led by three experts, and finally, the third step is a questionnaire with 605 users to compare the results between specialists and real users. This chapter aims to present the problems associated with these privacy settings, and it also wants to contribute for improving the user interaction with this social network.

scholarly journals A study of neighbour selection strategies for POI recommendation in LBSNs

2018 ◽  
Vol 44 (6) ◽  
pp. 802-817 ◽  
Author(s):  
Carlos Rios ◽  
Silvia Schiaffino ◽  
Daniela Godoy
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Location Based Services ◽  
Geographical Information ◽  
Good Prediction ◽  
General Terms ◽  
The Impact ◽  
Filtering Approach

Location-based recommender systems (LBRSs) are gaining importance with the proliferation of location-based services provided by mobile devices as well as user-generated content in social networks. Collaborative approaches for recommendation rely on the opinions of like-minded people, so-called neighbours, for prediction. Thus, an adequate selection of such neighbours becomes essential for achieving good prediction results. The aim of this work is to explore different strategies to select neighbours in the context of a collaborative filtering–based recommender system for POI (places of interest) recommendations. Whereas standard methods are based on user similarity to delimit a neighbourhood, in this work several strategies are proposed based on direct social relationships and geographical information extracted from location-based social networks (LBSNs). The impact of the different strategies proposed has been evaluated and compared against the traditional collaborative filtering approach using a dataset from a popular network as Foursquare. In general terms, the proposed strategies for selecting neighbours based on the different elements available in a LBSN achieve better results than the traditional collaborative filtering approach. Our findings can be helpful both to researchers in the recommender systems area and to recommender system developers in the context of LBSNs, since they can take into account our results to design and provide more effective services considering the huge amount of knowledge produced in LBSNs.

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