18,241 research outputs found
User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy
Recommender systems have become an integral part of many social networks and
extract knowledge from a user's personal and sensitive data both explicitly,
with the user's knowledge, and implicitly. This trend has created major privacy
concerns as users are mostly unaware of what data and how much data is being
used and how securely it is used. In this context, several works have been done
to address privacy concerns for usage in online social network data and by
recommender systems. This paper surveys the main privacy concerns, measurements
and privacy-preserving techniques used in large-scale online social networks
and recommender systems. It is based on historical works on security,
privacy-preserving, statistical modeling, and datasets to provide an overview
of the technical difficulties and problems associated with privacy preserving
in online social networks.Comment: 26 pages, IET book chapter on big data recommender system
Towards Psychometrics-based Friend Recommendations in Social Networking Services
Two of the defining elements of Social Networking Services are the social
profile, containing information about the user, and the social graph,
containing information about the connections between users. Social Networking
Services are used to connect to known people as well as to discover new
contacts. Current friend recommendation mechanisms typically utilize the social
graph. In this paper, we argue that psychometrics, the field of measuring
personality traits, can help make meaningful friend recommendations based on an
extended social profile containing collected smartphone sensor data. This will
support the development of highly distributed Social Networking Services
without central knowledge of the social graph.Comment: Accepted for publication at the 2017 International Conference on AI &
Mobile Services (IEEE AIMS
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