Nowadays, due to the high availability of heterogeneous data sources that can provide interesting information, users usually suffer from information overload. Therefore, the development of adaptive information systems that can offer personalized information and filter out irrelevant data for a user is required. Significant work has been developed to solve this problem in the area of the so-called recommendation systems. However, context information has only started to be considered recently to build recommendation systems, despite being key to obtain more accurate recommendations. Moreover, even with some context information, there is still a significant gap between the fields of mobile computing and recommendation systems.   In this paper, we focus on push-based recommendations (i.e., recommendations not explicitly requested) for mobile users, as it represents the most challenging and effective approach for recommending items in mobile environments. As opposed to existing work, a generic model that fits different domains is proposed. This model is based on the definition of the concept of environment and manages the impact of dynamic events and all the actors involved in the mobile recommendation process.
['Ramón Hermoso', 'Sergio Ilarri', 'Raquel Trillo', 'María del Carmen Rodríguez-Hernández']
Push-Based Recommendations in Mobile Computing Using a Multi-Layer Contextual Approach