Fog computing has emerged to support the requirements of IoT applications that could not be met by today's solutions. Different initiatives have been presented to drive the development of fog, and much work has been done to improve certain aspects. However, an in-depth analysis of the different solutions, detailing how they can be integrated and applied to meet specific requirements, is still required. In this work, we present a unified architectural model and a new taxonomy, by comparing a large number of solutions. Finally, we draw some conclusions and guidelines for the development of IoT applications based on fog.

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Abstract. Mobile devices have become increasingly popular in the everyday life of many individuals. By taking an insight into the most common uses of mobile devices we clearly appreciate that accessing internetbased services has grown greatly. This, and the fact that they are extremely personal gadgets has turned them into the main interface used by individuals to express themselves towards the outside world and to receive information from others. As a result of the highly personal use, mobile devices have been granted the potential to become unrivaled devices for building and storing the virtual profiles of their owners. Access to such profiles is of great interest in fields such as governance, health, smart cities, etc. Generating a centralized profile of a user is a task upon which a lot of interest has been put in the field of social mining. Peopleas-a-Service (PeaaS) is a computing model that seeks to establish the foundations upon which technologies that rely on mobile-centric computing models for social purposes should evolve.

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Depending on the Internet as the main source of information regarding all aspects of our life is becoming a trend. People seek relevant information, suggestions, and recommendations in an overloaded online world and through social ties regarding their daily activities, including places to visit and restaurants to try new food. The wide variety of choices that are available online causes information overloading, which thereby complicates the selection process. Traditional recommender systems are mostly dependent on a conventional model that is based on user-item-rating interaction without considering contextual information. We claim that new generations of recommendation systems able to exploit context in an innovative and efficient way is important and may statistically yield more significant rating predictions. However, only few research works have focused on how to effectively and efficiently exploit context metadata in Deep Learning (DL)-based recommendations. The main reason lies, perhaps most significantly, in the fact that most current DL algorithms are not intrinsically designed to incorporate contextual tags. In this paper, we provide a significant contribution for filling this gap by designing a hybrid algorithm that retrofits and repurposes a prefiltering contextual incorporation method and feeds the new dimension to a DL-based neural collaborative filtering method, thus preserving and recovering the benefits of both without their limitations. The paper also reports quantitative results that show that our method outperforms the baselines by statistically significant margins. INDEX TERMS Deep learning, recommender systems, collaborative filtering, context awareness, apache spark.

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