Managing Security in Aspect Oriented Recommender System

Abstract

Recommender systems are widely used in intelligent applications which assist users in a decision-making process to choose one item amongst a potentially overwhelming set of alternative products or services. A recommender system compares the user's profile to some reference characteristics. These characteristics may be from the information item (the content-based approach) or the user's social environment (the collaborative filtering approach) or a combination of both (Hybrid-filtering approach). Recent research shows that collaborative recommender systems are highly vulnerable to profiles injection attacks. Therefore, security mechanisms are needed for protecting the recommender systems against these attacks. Aspect Oriented Recommender System (AORS) is a proposed multi agent system (MAS) that uses the concept of Aspect Oriented Programming (AOP) for building security aspect. Implementing the security in recommender system using a conventional agent oriented approach results not only with the problem of code scattering and code tangling, but also results in weaker enforcement of security concern. In this paper, security crosscutting is handled as aspect in AORS in a modular way to remove scattering and tangling problems. The prototype of AORS has been designed and developed for a book recommender system.