A Systematic Review of Recommender Systems and Their Applications in Cybersecurity

Sensors and Pattern Recognition Methods for Security and Industrial Applications

Contemporary cyberthreats continue to evolve, powering the neverending development arms race [...].

A Deterministic Model for Determining Degree of Friendship Based on Mutual Likings and Recommendations on OTT Platforms

In recent years, the application of various recommendation algorithms on over-the-top (OTT) platforms such as Amazon Prime and Netflix has been explored, but the existing recommendation systems are less effective because either they fail to take an advantage of exploiting the inherent user relationship or they are not capable of precisely defining the user relationship. On such platforms, users generally express their preferences for movies and TV shows and also give ratings to them. For a recommendation system to be effective, it is important to establish an accurate and precise relationship between the users. Hence, there is a scope of research for effective recommendation systems that can define a relationship between users and then use the relationship to enhance the user experiences. In this research article, we have presented a hybrid recommendation system that determines the degree of friendship among the viewers based on mutual liking and recommendations on OTT platforms. The proposed enhanced model is an effective recommendation model for determining the degree of friendship among viewers with improved user experience.

User-Centric Privacy for Identity Federations Based on a Recommendation System

Specifications such as SAML, OAuth, OpenID Connect and Mobile Connect are essential for solving identification, authentication and authorisation in contexts such as mobile apps, social networks, e-commerce, cloud computing or the Internet of Things. However, end-users relying on identity providers to access resources, applications or services lose control over the Personally Identifiable Information (PII) they share with the different providers composing identity federations. This work proposes a user-centric approach based on a recommendation system to support users in making privacy decisions such as selecting service providers or choosing their privacy settings. The proposed Privacy Advisor gives end-users privacy protection by providing personalised recommendations without compromising the identity federations’ functionalities or requiring any changes in their underlying specifications. A proof of concept of the proposed recommendation system is presented to validate and evaluate its utility and feasibility.