User modeling framework for context-aware recommender systems

Abstract

Context-aware recommender systems (CARS) use data about the user and the context to enhance their recommendation outcomes, such data is stored in user models. As the is no generic data model, CARS developers and researchers need to design and develop their own model, with no model to use as reference, nor any tool that facilitate the design and development work. In this work we present a user modeling framework for context-aware recommender systems whose core is a generic user model for CARS. The framework is intended to facilitate the implementation of the models by providing a pre-implemented, working ready functionality, while the model itself can be used by developers and researchers as a basis while creating more specialized models.