Eating is never just a matter of the palate – it is a medial, technical, and cultural event. This issue explores how digital sensors, AI, and social media are reshaping our perception of taste, smell, and texture. From electronic noses and machine tasting to food photography and AI-generated recipes, culinary culture emerges as a field where senses, media, and technologies intersect in new ways.

The rapid digitalization of the information age has led to challenges such as information overload, underscoring the critical role of Recommender Systems (RecSys) in organizing and delivering relevant content. Despite their ubiquity, the development of RecSys remains resource-intensive and labor-intensive. Additionally, the environmental impact of RecSys has emerged as a critical yet underexplored concern as modern approaches increasingly employ resource-intensive architectures. The field of Automated Machine Learning (AutoML) has demonstrated significant success in streamlining model development for general machine learning tasks, lowering the barrier of entry for researchers and practitioners by reducing necessary manual labor and expertise while increasing performance. Motivated by their advancements, we investigate the transfer of AutoML principles to RecSys in the framework of Automated Recommender Systems (AutoRecSys) in this dissertation. We focus on solving the automated algorithm selection problem, due to its high relevance for efficient RecSys modeling, and address significant optimization confounders in AutoRecSys. We propose an algorithm selection framework that addresses dataset limitations through community-contributed metadata while offloading computationally intensive meta-learning tasks to server-side components for efficient client-side deployment. Additionally, we provide the first analysis of algorithm selection for ranking prediction tasks with implicit feedback datasets, showing the correlation with ground-truth performance for traditional and AutoML-based meta-models. We quantify the impact of randomness during data splitting, showing that it leads to significant performance deviations unless mitigated through repeated experiments or cross-validation. Furthermore, we analyze the suitability of top-N metrics for optimization, showing that their use in validation does not introduce confounding effects in RecSys evaluation. This reinforces the reliability of conventional evaluation methodologies. Beyond automation and evaluation concerns, we conduct the first comprehensive investigation of the environmental impact of RecSys experiments. We reveal that modern deep learning-based papers emit 42 times more CO2 equivalents than papers employing traditional approaches. Furthermore, we introduce a software tool for measuring and reporting energy consumption in RecSys experiments, enabling researchers to understand and report their environmental impact. Finally, we synthesize our contributions and demonstrate that automated algorithm selection can amortize its environmental impact through widespread adoption. To summarize, this dissertation lays the foundation for future research in algorithm selection through the AutoRecSys framework, further reduces uncertainties for RecSys evaluation methodologies, and helps the RecSys community to understand and address their environmental impact for a sustainable future.

Inspired by biological neural networks, neuromorphic computing has emerged as a promising alternative to conventional architectures, which increasingly struggle to meet the needs of low power consumption, parallel processing, and real-time operation. The realisation of neuromorphic systems requires new types of devices, which have the capability of simultaneously storing and processing information. It is, however, not easily available from conventional Complementary Metal-Oxide-Semiconductor (CMOS)-based technologies. Memristor is a promising candidate for such systems, owing to its synapse-like, tunable resistance states and compatibility with CMOS technology. This cumulative dissertation investigates the fabrication, characterisation, and integration of memristor for neuromorphic and optical applications. For practical integration, the materials and fabrication techniques have been selected to be compatible with standard CMOS technology. The work first focuses on understanding the bidirectional switching behaviour of individual redox-based memristors and examines how the dielectric layer thickness influences device performance. Following these, a experimental realisation of an intelligent photodetector is achieved, which integrates a photodiode with a memristor to enable adaptive passive quenching. Meanwhile, a CMOS-compatible photodiode operating in Geiger mode is also designed for this purpose. Furthermore, this dissertation explores the Fermi–Pasta–Ulam–Tsingou (FPUT) system under mismatched condition, thereby addressing the theoretical foundations for building a nonlinear system with mismatch. Tolerance effects are analysed using Monte Carlo simulations, and a method is proposed to recover the energy recurrence behaviour despite such variability. This dissertation includes five peer-reviewed journal and conference papers that combine numerical and experimental research. Through the co-design of novel devices and non-linear systems, these works collectively lay the foundation for the future development of neuromorphic systems.

Das vorliegende Dossier erläutert zunächst, was Recommendersysteme sind und wie sie technisch umgesetzt werden. Nachfolgend wird aufgezeigt, zu welchem Zweck Recommendersysteme beim technologiegestützten Lernen eingesetzt werden – sowohl im Bildungsbereich allgemein als auch speziell in der beruflichen Weiterbildung. Der größere Teil dieses Dossiers widmet sich anschließend spezifischen Herausforderungen der Entwicklung und Implementierung konkreter Recommendersysteme auf digitalen Weiterbildungsplattformen. Dabei werden basierend auf der bestehenden Literatur und Aussagen von Expert_innen Handlungsempfehlungen aufgeführt. Insgesamt soll das vorliegende Dossier damit den Einsatz von Recommendersystemen in der beruflichen Aus- und Weiterbildung sowohl aus technischer als auch didaktischer Perspektive beleuchten.