## 1 Introduction  
Personalization and recommender systems have spread thanks to the growth of heterogeneous connected devices and their increasing possibilities. Daily we carry with us different smart objects whose capabilities allow them to be aware of the environment. These devices can actually learn from experience. They are aware of our behavior, preferences and trends. This new and rich situation enhances and improves the way users interact with context, but it also makes people more dependent in the use of these technologies. This also means that users are becoming a significant and active part of the Ambient Assisted Living (AAL) and Pervasive Computing environments. For example, we can obtain recommendations while shopping based on the previously purchased items, change the music based on the user's mood, get notifications of new and interesting releases, receive different alternatives to reach some places considering our current location, even get social advices about people consuming the same or similar products or services. Besides, this is not a situation which concerns only researchers. Local governments and public administrations have discovered the importance of working with context data [1]. This way, they try to improve citizens' satisfaction through new and smarter infrastructures.  
On the other hand, application and service personalization has abandoned the idea of being just tools or frameworks to allow users to customize different resources and devices. Actually it involves the perspective of inclusive design, whose main purpose is to avoid side lining smaller groups of users, like people with disabilities and elders [2]. These groups are known for suffering from several limitations. Moreover, although nowadays the share of people aged 65 represent a 17% of the current European population, by the year 2060 this figure is projected to rise to 30% [3]. As a consequence, and as the European Commission states, _\"the EU would move from having four people of working-age to each person aged over 65 years to about two people of working-age\"_. Evidently the current situation shows that it is still a small group, but with a high expected increasing ratio. Nevertheless, this implies that we are still in time of accommodating, adapting and overtaking for future economic and demographic consequences.  
Then, what is the right way of considering context, users and devices to perform these recommendations and adaptations for the user? The answer is _modeling_. Modeling these entities allow researchers and developers to consider different conditions that might trigger several recommendations, adaptations or services to satisfy the user needs. User's interests are useful for recommending systems [4]. On the other hand, their medical capabilities might be needed for adaptive environments [5]. The same occurs when we consider context or devices.  
As we will see later in Section 2, during the past 15 years there has been a lot of work done in this area. Authors have followed different approaches and developed different techniques to take into account every possible scenario. In this paper, we analyze these solutions studying their advantages and disadvantages to, finally, discuss about the evolution of these systems and about the future of modeling.  
The remainder of this paper is structured as follows: First, in Section 1.1 we introduce our motivation for this paper to detail the perspective from which this work should be considered. In Section 2 we review the literature solutions for modeling context, users and devices during the past 15 years. Within each subsection of the state of the art an analysis of the considered literature solutions is presented. Several standardization works are also remarked. Next, Section 3 discusses about the presented approaches andremarks several problems and future issues to be taken into account. Finally, in Section 4, we summarize our experiences and discuss the conclusions.