As the emerging Internet of Things (IoT) makes an increasing ammout of connected services accessible to a broad range of users, the identification of contextually relevant services becomes an indispensable task. In order to evaluate existing methods for contextual IoT service recommendation, we develop an ambient space simulation to emulate huge numbers of IoT services and user interactions. Secondly we investigate a new extension of the previously evaluated single IoT service recommendation

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... e recommendation of composite services consisting of multiple services to perform a mutual task. To address this challenge, we construct and implement a framework called ConComM to identify services that are likely to work well together for a joint task. Our previously developed ambient space simulation is then used to evaluate our frameworks performance. The framework itself utilizes a novel k-cut algorithm based on a modification of the existing k-cut procedure SPLIT. We call this new procedure SPLIT rel and show it to outperform all tested benchmark algorithms for minimum k-cuts on graphs used by ConComM. Our experiments prove ConComM to significantly push the performance of an existing single service recommendation approach for composite service recommendation. Within this work we do not only provide a simulation that allows to evaluate recommendation systems in environments densly filled with IoT services. We also develop a framework that enables contextual bandit algorithms to provide improved recommendations for composite services. * This section is part of the propaedeuticum. † Users can explicitly be male and female. As the B.Sc. students in the main autors field of studies are male in 75% of the cases, we will address users as male individuals in this thesis.