Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMsThis research work was partially supported by the Sejong University Research Faculty Program (20212023)S

Machine learning for managing structured and semi-structured data

As the digitalization of private, commercial, and public sectors advances rapidly, an increasing amount of data is becoming available. In order to gain insights or knowledge from these enormous amounts of raw data, a deep analysis is essential. The immense volume requires highly automated processes with minimal manual interaction. In recent years, machine learning methods have taken on a central role in this task. In addition to the individual data points, their interrelationships often play a decisive role, e.g. whether two patients are related to each other or whether they are treated by the same physician. Hence, relational learning is an important branch of research, which studies how to harness this explicitly available structural information between different data points. Recently, graph neural networks have gained importance. These can be considered an extension of convolutional neural networks from regular grids to general (irregular) graphs. Knowledge graphs play an essential role in representing facts about entities in a machine-readable way. While great efforts are made to store as many facts as possible in these graphs, they often remain incomplete, i.e., true facts are missing. Manual verification and expansion of the graphs is becoming increasingly difficult due to the large volume of data and must therefore be assisted or substituted by automated procedures which predict missing facts. The field of knowledge graph completion can be roughly divided into two categories: Link Prediction and Entity Alignment. In Link Prediction, machine learning models are trained to predict unknown facts between entities based on the known facts. Entity Alignment aims at identifying shared entities between graphs in order to link several such knowledge graphs based on some provided seed alignment pairs. In this thesis, we present important advances in the field of knowledge graph completion. For Entity Alignment, we show how to reduce the number of required seed alignments while maintaining performance by novel active learning techniques. We also discuss the power of textual features and show that graph-neural-network-based methods have difficulties with noisy alignment data. For Link Prediction, we demonstrate how to improve the prediction for unknown entities at training time by exploiting additional metadata on individual statements, often available in modern graphs. Supported with results from a large-scale experimental study, we present an analysis of the effect of individual components of machine learning models, e.g., the interaction function or loss criterion, on the task of link prediction. We also introduce a software library that simplifies the implementation and study of such components and makes them accessible to a wide research community, ranging from relational learning researchers to applied fields, such as life sciences. Finally, we propose a novel metric for evaluating ranking results, as used for both completion tasks. It allows for easier interpretation and comparison, especially in cases with different numbers of ranking candidates, as encountered in the de-facto standard evaluation protocols for both tasks.Mit der rasant fortschreitenden Digitalisierung des privaten, kommerziellen und öffentlichen Sektors werden immer größere Datenmengen verfügbar. Um aus diesen enormen Mengen an Rohdaten Erkenntnisse oder Wissen zu gewinnen, ist eine tiefgehende Analyse unerlässlich. Das immense Volumen erfordert hochautomatisierte Prozesse mit minimaler manueller Interaktion. In den letzten Jahren haben Methoden des maschinellen Lernens eine zentrale Rolle bei dieser Aufgabe eingenommen. Neben den einzelnen Datenpunkten spielen oft auch deren Zusammenhänge eine entscheidende Rolle, z.B. ob zwei Patienten miteinander verwandt sind oder ob sie vom selben Arzt behandelt werden. Daher ist das relationale Lernen ein wichtiger Forschungszweig, der untersucht, wie diese explizit verfügbaren strukturellen Informationen zwischen verschiedenen Datenpunkten nutzbar gemacht werden können. In letzter Zeit haben Graph Neural Networks an Bedeutung gewonnen. Diese können als eine Erweiterung von CNNs von regelmäßigen Gittern auf allgemeine (unregelmäßige) Graphen betrachtet werden. Wissensgraphen spielen eine wesentliche Rolle bei der Darstellung von Fakten über Entitäten in maschinenlesbaren Form. Obwohl große Anstrengungen unternommen werden, so viele Fakten wie möglich in diesen Graphen zu speichern, bleiben sie oft unvollständig, d. h. es fehlen Fakten. Die manuelle Überprüfung und Erweiterung der Graphen wird aufgrund der großen Datenmengen immer schwieriger und muss daher durch automatisierte Verfahren unterstützt oder ersetzt werden, die fehlende Fakten vorhersagen. Das Gebiet der Wissensgraphenvervollständigung lässt sich grob in zwei Kategorien einteilen: Link Prediction und Entity Alignment. Bei der Link Prediction werden maschinelle Lernmodelle trainiert, um unbekannte Fakten zwischen Entitäten auf der Grundlage der bekannten Fakten vorherzusagen. Entity Alignment zielt darauf ab, gemeinsame Entitäten zwischen Graphen zu identifizieren, um mehrere solcher Wissensgraphen auf der Grundlage einiger vorgegebener Paare zu verknüpfen. In dieser Arbeit stellen wir wichtige Fortschritte auf dem Gebiet der Vervollständigung von Wissensgraphen vor. Für das Entity Alignment zeigen wir, wie die Anzahl der benötigten Paare reduziert werden kann, während die Leistung durch neuartige aktive Lerntechniken erhalten bleibt. Wir erörtern auch die Leistungsfähigkeit von Textmerkmalen und zeigen, dass auf Graph-Neural-Networks basierende Methoden Schwierigkeiten mit verrauschten Paar-Daten haben. Für die Link Prediction demonstrieren wir, wie die Vorhersage für unbekannte Entitäten zur Trainingszeit verbessert werden kann, indem zusätzliche Metadaten zu einzelnen Aussagen genutzt werden, die oft in modernen Graphen verfügbar sind. Gestützt auf Ergebnisse einer groß angelegten experimentellen Studie präsentieren wir eine Analyse der Auswirkungen einzelner Komponenten von Modellen des maschinellen Lernens, z. B. der Interaktionsfunktion oder des Verlustkriteriums, auf die Aufgabe der Link Prediction. Außerdem stellen wir eine Softwarebibliothek vor, die die Implementierung und Untersuchung solcher Komponenten vereinfacht und sie einer breiten Forschungsgemeinschaft zugänglich macht, die von Forschern im Bereich des relationalen Lernens bis hin zu angewandten Bereichen wie den Biowissenschaften reicht. Schließlich schlagen wir eine neuartige Metrik für die Bewertung von Ranking-Ergebnissen vor, wie sie für beide Aufgaben verwendet wird. Sie ermöglicht eine einfachere Interpretation und einen leichteren Vergleich, insbesondere in Fällen mit einer unterschiedlichen Anzahl von Kandidaten, wie sie in den de-facto Standardbewertungsprotokollen für beide Aufgaben vorkommen

Valoriser les connaissances issues des expériences vécues pour recommander des actions de protection des sources d'eau potable : application du raisonnement à base de cas

Depuis des décennies, les pays du monde entier s'affairent à préserver leurs précieuses ressources en eau potable. Ils cherchent à anticiper les risques et à réduire les impacts anthropiques qui pourraient altérer les sources d'approvisionnement. Au Canada, la protection des sources d'eau potable (PSEP) est mise en œuvre au sein de l'approche à barrières multiples, dont elle est l'une des barrières fondamentales. Cette approche permet une gestion multidimensionnelle de l'eau à l'aide d'outils et des pratiques visant à assurer la qualité de l'eau de la source au robinet. Bien que différents cadres existent pour prendre en compte l'eau dans l'aménagement du territoire, la mise en œuvre de la protection des sources peine à se concrétiser. Pourtant, les acteurs de l'eau et de l'aménagement du territoire ont une grande expérience dans la mise en œuvre d'actions. Alors, comment partager ces expériences afin de les soutenir dans l'identification et la mise en œuvre de futures actions de protection des sources ? Le but de cette thèse est de développer un prototype de système d'aide à la décision à base de connaissances (KB-DSS). Celui-ci a pour objectif de faciliter l'identification d'actions ciblées de PSEP selon les problèmes rencontrés. Pour ce faire, ce prototype a été développé sur la base des connaissances issues des expériences vécues depuis deux décennies au Québec, mettant à contribution des expériences réelles de mise en œuvre d'actions liées à la protection de l'eau. Il est conçu pour être utilisable par tout acteur ayant un intérêt à agir pour protéger les sources d'eau potable à l'échelle locale et régionale, via un transfert de connaissances dans le processus d'élaboration et de mise en œuvre d'actions. En étant un support dans la définition des actions futures, le prototype développé entend encourager les parties prenantes à apprendre les unes des autres. L'originalité de la thèse repose sur l'adoption combinée de l'approche en science du design/de la conception (DSR), qui a servi de lignes directrices pour adopter une démarche collaborative et transparente. Celle-ci a permis une application réussie du raisonnement à base de cas (CBR) au complexe problème de la PSEP dans un cadre de gestion de l'eau et du territoire. De cette démarche sont nés différents outils méthodologiques, procédures et connaissances permettant de mieux comprendre les problèmes liés à la PSEP, mais également d'illustrer la conception intégrale d'un prototype d'aide à la décision à base de connaissances utilisant le CBR. Tout d'abord, le cadre conceptuel (chapitre 1) explore et tente de comprendre les liens qui existent entre la nature des problèmes à résoudre pour protéger l'eau, l'environnement décisionnel et la prise de décision. Pour ce faire, le cadre adopte une approche systémique et holistique superposant différentes théories et concepts tels que la gouvernance de l'eau, la gestion de l'eau, la prise de décision, la rationalité et la connaissance. Cette compréhension des défis sous-jacents à la mise en œuvre de la PSEP permettait de mieux comprendre la complexité du problème à résoudre et posait les bases à l'élaboration du prototype de système CBR proposé. Dans l'optique de mieux comprendre comment les défis soulevés dans le cadre conceptuel se concrétisent en pratique, le second chapitre présente une enquête en ligne documentant la mise en œuvre de la PSEP au Québec. Celle-ci visait à brosser un portrait-diagnostic permettant de mieux comprendre le processus décisionnel, d'identifier qui sont les intervenants et quelles sont les connaissances produites et mobilisées pour la prise de décision sur la PSEP. Les analyses qualitatives et quantitatives des réponses des 208 intervenants retenus ont permis de constater que la mise en œuvre de la PSEP impliquait une grande diversité d'intervenants, de tâches et de connaissances créées et se caractérisait par un fort dynamisme inter-organisationnel. Cependant, on constatait que son processus décisionnel perdait en inclusivité au fil des étapes de mise en œuvre, que les connaissances étaient parfois redondantes et qu'il existait de nombreux enjeux de transfert de connaissances (accès, quantité ou qualité des connaissances) entre les intervenants. Lors de l'enquête en ligne présentée au second chapitre, il a été demandé à certains acteurs (organismes de bassins versants, villes, municipalités régionales de comté) d'illustrer les problèmes liés à la PSEP rencontrés sur le terrain. En parallèle, 102 intervenants se sont auto-recrutés pour participer au processus de design du système d'aide à la décision. Le troisième chapitre présente la démarche d'acquisition et de structuration des connaissances du dit KB-DSS par une approche CBR. Le chapitre décrit une seconde enquête en ligne ayant permis de définir ce qu'est un cas pour la PSEP, soit une expérience vécue qui consiste en une multitude de problèmes et de solutions mises en œuvre. Puis, il décrit la modélisation d'une taxonomie des connaissances ayant permis d'aboutir à des descriptions structurées des cas. La conception des cas repose sur le savoir-faire et les besoins en connaissances exprimés par les acteurs de l'eau. La base de cas constitue l'épine dorsale du prototype de KB-DSS destiné à guider les décideurs dans l'élaboration de solutions fondées sur des expériences passées. Le quatrième chapitre présente le prototype de KB-DSS/CBR pour la protection des sources d'eau potable. Il retrace comment le CBR a été modélisé, structuré, implanté, testé et validé en collaboration avec les 102 acteurs de la gestion et de la gouvernance de l'eau au Québec. Il décrit l'intégralité du processus manuel d'ingénierie de cas pour concevoir des attributs qualitatifs sur la base de la taxonomie des connaissances. Il présente l'édition des cas, le processus et les métriques permettant de retrouver des cas, l'implantation et un exemple d'utilisation ainsi que la validation du prototype, réalisée par une procédure participative rigoureuse et transparente avec un petit groupe d'acteurs de l'eau du Québec. Ainsi, il fournit des preuves empiriques du potentiel positif d'une approche CBR pour la PSEP sur le territoire, et retrace une démarche qui peut être généralisée à d'autres contextes géographiques et socio-économiques similaires.Countries worldwide have been working for decades to preserve their precious drinking water resources. They seek to anticipate risks or reduce anthropogenic impacts that could alter the water quality and availability. In Canada, drinking water source protection (DSWP), or source water protection (SWP), is implemented as part of the multi-barrier approach and is one of the fundamental barriers. This approach allows for multidimensional water management using tools and practices to ensure water quality from source to tap. Although various frameworks exist to consider water in spatial planning, the implementation of DWSP is struggling to materialize. However, water and spatial planning actors have significant experience implementing actions. So, how can these experiences be shared to support them in identifying and implementing future DWSP actions? The goal of this thesis is to develop a prototype of a knowledge-based decision support system (KB-DSS). The objective of this prototype is to facilitate the identification of targeted actions for water protection according to the problems encountered. To do so, this prototype was developed based on knowledge gained from past experiences conducted over the last two decades in Quebec, using real experiences in implementing actions related to water protection. It is designed to be used by any actor with an interest in contributing for the protection of drinking water sources at the local and regional levels, through the transfer of knowledge in the process of developing and implementing actions. By being a support in the definition of future actions, the developed prototype intends to encourage the actors to learn from each other. The originality of the thesis lies in the combined adoption of the design science approach (DSR), which served as a guideline to adopt a collaborative and transparent approach. This allowed for a successful application of case-based reasoning (CBR) to the complex problem of DWSP in a water and territory management framework. From this approach, various methodological tools, procedures and knowledge were developed to better understand the DWSP problems, but also to illustrate the complete design of a prototype knowledge-based decision support system using CBR. First, the conceptual framework (chapter 1) explores and attempts to understand the links between the nature of the problems to be solved to protect water, the decision-making environment, and the decision-making process. These issues were explored by adopting a system analysis that allowed for layering concepts such as water governance, water management, decision-making, rationality, and knowledge. This holistic understanding of the underlying challenges of DWSP implementation provided a better understanding of the complexity of the problem at hand and laid the foundation for developing the proposed CBR system. To better understand how the challenges raised in the conceptual framework materialize in practice, the second chapter presents an online survey documenting the implementation of DWSP in Quebec. This survey aimed to provide a diagnostic portrait to understand the decision-making process better and identify the actors and the knowledge produced and mobilized for DWSP decision-making. Qualitative and quantitative analyses of the responses from the 208 selected actors revealed that the implementation of DWSP involved a wide variety of actors, tasks and knowledge created and was characterized by great inter-organizational dynamism. However, it was found that the decision-making process becomes less inclusive as actions are implemented. Also, the knowledge was sometimes redundant, and there were many problems with the knowledge transfer (access, quantity, or knowledge quality) between actors. During the online survey presented in the second chapter, selected actors (watershed organizations, municipalities, counties, etc.) were asked to illustrate DWSP-related problems encountered in the field. In parallel, 102 actors were self-recruited to participate in the design process of the KB-DSS. The third chapter presents the acquisition and structuring of DWSP problem-related knowledge. The chapter describes a second online survey that helped define a DWSP case, i.e., a lived experience consisting of a multitude of problems and solutions implemented at various scales by various actors. It then describes the modelling of a knowledge taxonomy that led to structured case descriptions. The design of the cases is based on the expertise and knowledge needs expressed by the water actors. The case base is the backbone of the KB-DSS prototype to guide decision-makers in developing solutions based on past experiences. The fourth chapter presents the prototype KB-DSS/CBR system for DWSP. It traces how CBR was modelled, structured, implemented, tested and validated in collaboration with 102 water management and governance actors in Quebec. It describes the entire manual case engineering process for the design of qualitative attributes from the knowledge taxonomy. It presents the case base, the case edition, and the case retrieval (process and metrics). This chapter also illustrates the implementation using a real-world experience use case, as well as the validation of the prototype, carried out through a transparent, participatory procedure with a small group of water actors in Quebec. Thus, it provides empirical evidence of the high potential of a CBR approach for DWSP in the spatial planning context and describes an approach that can be generalized to other similar geographical and socio-economic contexts

Languages of games and play: A systematic mapping study

Digital games are a powerful means for creating enticing, beautiful, educational, and often highly addictive interactive experiences that impact the lives of billions of players worldwide. We explore what informs the design and construction of good games to learn how to speed-up game development. In particular, we study to what extent languages, notations, patterns, and tools, can offer experts theoretical foundations, systematic techniques, and practical solutions they need to raise their productivity and improve the quality of games and play. Despite the growing number of publications on this topic there is currently no overview describing the state-of-the-art that relates research areas, goals, and applications. As a result, efforts and successes are often one-off, lessons learned go overlooked, language reuse remains minimal, and opportunities for collaboration and synergy are lost. We present a systematic map that identifies relevant publications and gives an overview of research areas and publication venues. In addition, we categorize research perspectives along common objectives, techniques, and approaches, illustrated by summaries of selected languages. Finally, we distill challenges and opportunities for future research and development

Building bridges for better machines : from machine ethics to machine explainability and back

Be it nursing robots in Japan, self-driving buses in Germany or automated hiring systems in the USA, complex artificial computing systems have become an indispensable part of our everyday lives. Two major challenges arise from this development: machine ethics and machine explainability. Machine ethics deals with behavioral constraints on systems to ensure restricted, morally acceptable behavior; machine explainability affords the means to satisfactorily explain the actions and decisions of systems so that human users can understand these systems and, thus, be assured of their socially beneficial effects. Machine ethics and explainability prove to be particularly efficient only in symbiosis. In this context, this thesis will demonstrate how machine ethics requires machine explainability and how machine explainability includes machine ethics. We develop these two facets using examples from the scenarios above. Based on these examples, we argue for a specific view of machine ethics and suggest how it can be formalized in a theoretical framework. In terms of machine explainability, we will outline how our proposed framework, by using an argumentation-based approach for decision making, can provide a foundation for machine explanations. Beyond the framework, we will also clarify the notion of machine explainability as a research area, charting its diverse and often confusing literature. To this end, we will outline what, exactly, machine explainability research aims to accomplish. Finally, we will use all these considerations as a starting point for developing evaluation criteria for good explanations, such as comprehensibility, assessability, and fidelity. Evaluating our framework using these criteria shows that it is a promising approach and augurs to outperform many other explainability approaches that have been developed so far.DFG: CRC 248: Center for Perspicuous Computing; VolkswagenStiftung: Explainable Intelligent System

Laws and Emerging Technologies

Technologies will have a huge impact on society in the coming years and will bring new challenges and legal challenges to the legal sector worldwide. On the other hand, the new communications era also brings many new legal issues, such as those derived from e-commerce and payment services, intellectual property, or the problems derived from the use of new technologies by young people

Educational Technology and Related Education Conferences for June to December 2015

The 33rd edition of the conference list covers selected events that primarily focus on the use of technology in educational settings and on teaching, learning, and educational administration. Only listings until December 2015 are complete as dates, locations, or Internet addresses (URLs) were not available for a number of events held from January 2016 onward. In order to protect the privacy of individuals, only URLs are used in the listing as this enables readers of the list to obtain event information without submitting their e-mail addresses to anyone. A significant challenge during the assembly of this list is incomplete or conflicting information on websites and the lack of a link between conference websites from one year to the next

Enhancing explainability and scrutability of recommender systems

Our increasing reliance on complex algorithms for recommendations calls for models and methods for explainable, scrutable, and trustworthy AI. While explainability is required for understanding the relationships between model inputs and outputs, a scrutable system allows us to modify its behavior as desired. These properties help bridge the gap between our expectations and the algorithm’s behavior and accordingly boost our trust in AI. Aiming to cope with information overload, recommender systems play a crucial role in filtering content (such as products, news, songs, and movies) and shaping a personalized experience for their users. Consequently, there has been a growing demand from the information consumers to receive proper explanations for their personalized recommendations. These explanations aim at helping users understand why certain items are recommended to them and how their previous inputs to the system relate to the generation of such recommendations. Besides, in the event of receiving undesirable content, explanations could possibly contain valuable information as to how the system’s behavior can be modified accordingly. In this thesis, we present our contributions towards explainability and scrutability of recommender systems: • We introduce a user-centric framework, FAIRY, for discovering and ranking post-hoc explanations for the social feeds generated by black-box platforms. These explanations reveal relationships between users’ profiles and their feed items and are extracted from the local interaction graphs of users. FAIRY employs a learning-to-rank (LTR) method to score candidate explanations based on their relevance and surprisal. • We propose a method, PRINCE, to facilitate provider-side explainability in graph-based recommender systems that use personalized PageRank at their core. PRINCE explanations are comprehensible for users, because they present subsets of the user’s prior actions responsible for the received recommendations. PRINCE operates in a counterfactual setup and builds on a polynomial-time algorithm for finding the smallest counterfactual explanations. • We propose a human-in-the-loop framework, ELIXIR, for enhancing scrutability and subsequently the recommendation models by leveraging user feedback on explanations. ELIXIR enables recommender systems to collect user feedback on pairs of recommendations and explanations. The feedback is incorporated into the model by imposing a soft constraint for learning user-specific item representations. We evaluate all proposed models and methods with real user studies and demonstrate their benefits at achieving explainability and scrutability in recommender systems.Unsere zunehmende Abhängigkeit von komplexen Algorithmen für maschinelle Empfehlungen erfordert Modelle und Methoden für erklärbare, nachvollziehbare und vertrauenswürdige KI. Zum Verstehen der Beziehungen zwischen Modellein- und ausgaben muss KI erklärbar sein. Möchten wir das Verhalten des Systems hingegen nach unseren Vorstellungen ändern, muss dessen Entscheidungsprozess nachvollziehbar sein. Erklärbarkeit und Nachvollziehbarkeit von KI helfen uns dabei, die Lücke zwischen dem von uns erwarteten und dem tatsächlichen Verhalten der Algorithmen zu schließen und unser Vertrauen in KI-Systeme entsprechend zu stärken. Um ein Übermaß an Informationen zu verhindern, spielen Empfehlungsdienste eine entscheidende Rolle um Inhalte (z.B. Produkten, Nachrichten, Musik und Filmen) zu filtern und deren Benutzern eine personalisierte Erfahrung zu bieten. Infolgedessen erheben immer mehr In- formationskonsumenten Anspruch auf angemessene Erklärungen für deren personalisierte Empfehlungen. Diese Erklärungen sollen den Benutzern helfen zu verstehen, warum ihnen bestimmte Dinge empfohlen wurden und wie sich ihre früheren Eingaben in das System auf die Generierung solcher Empfehlungen auswirken. Außerdem können Erklärungen für den Fall, dass unerwünschte Inhalte empfohlen werden, wertvolle Informationen darüber enthalten, wie das Verhalten des Systems entsprechend geändert werden kann. In dieser Dissertation stellen wir unsere Beiträge zu Erklärbarkeit und Nachvollziehbarkeit von Empfehlungsdiensten vor. • Mit FAIRY stellen wir ein benutzerzentriertes Framework vor, mit dem post-hoc Erklärungen für die von Black-Box-Plattformen generierten sozialen Feeds entdeckt und bewertet werden können. Diese Erklärungen zeigen Beziehungen zwischen Benutzerprofilen und deren Feeds auf und werden aus den lokalen Interaktionsgraphen der Benutzer extrahiert. FAIRY verwendet eine LTR-Methode (Learning-to-Rank), um die Erklärungen anhand ihrer Relevanz und ihres Grads unerwarteter Empfehlungen zu bewerten. • Mit der PRINCE-Methode erleichtern wir das anbieterseitige Generieren von Erklärungen für PageRank-basierte Empfehlungsdienste. PRINCE-Erklärungen sind für Benutzer verständlich, da sie Teilmengen früherer Nutzerinteraktionen darstellen, die für die erhaltenen Empfehlungen verantwortlich sind. PRINCE-Erklärungen sind somit kausaler Natur und werden von einem Algorithmus mit polynomieller Laufzeit erzeugt , um präzise Erklärungen zu finden. • Wir präsentieren ein Human-in-the-Loop-Framework, ELIXIR, um die Nachvollziehbarkeit der Empfehlungsmodelle und die Qualität der Empfehlungen zu verbessern. Mit ELIXIR können Empfehlungsdienste Benutzerfeedback zu Empfehlungen und Erklärungen sammeln. Das Feedback wird in das Modell einbezogen, indem benutzerspezifischer Einbettungen von Objekten gelernt werden. Wir evaluieren alle Modelle und Methoden in Benutzerstudien und demonstrieren ihren Nutzen hinsichtlich Erklärbarkeit und Nachvollziehbarkeit von Empfehlungsdiensten