Improving Adaptation Rules Quality Using Genetic Programming

Personalized Information System (PIS) aims to provide tailored services to users in various contexts. The aim of such system is to help users find relevant content easier and faster. To achieve such behaviour, the system needs a user model providing information about users, e.g., about their interests, skills, background and custom information while ensuring their adaptation to the needs and preferences of each user. This system is able to learn about the preferences of individual users and to tailor the content, interface, and behaviour to the user preferences. In fact, the diversity of contexts and the proliferation of profiles make personalization a very sophisticated process. Personalization is a major challenge for the information system. In fact, its quality and its adaptation to the user's preferences represent a key of success of these systems. In this context, this paper presents a personalized method based on the principle of genetic programming by extracting the best adaptation rules.