Summer hot, Winter not! – Seasonal influences on context-based music recommendations

This paper explores artefact generativity as a novel conceptual frame to inform information system (IS) artefact design beyond designing for artefact utility at the time of an artefact’s introduction or implementation. The paper draws on three recently developed generativity conceptualizations and applies the findings to IS artefact design. Artefact generativity captures the notions that 1) sustained artefact utility may require potentially continuous artefact changes over its lifetime, 2) (re-)designers need to enact these changes within a design system, and 3) continuous artefact use may lead to further generative transformations in the artefact’s social and technical environment. IS design science researchers can draw on this paper’s findings to inform future artefact design decisions that address these three notions by drawing on the established and growing foundations of IS and social science theories that underlie the established generativity perspectives

A model for context awareness for mobile applications using multiple-input sources

Context-aware computing enables mobile applications to discover and benefit from valuable context information, such as user location, time of day and current activity. However, determining the users’ context throughout their daily activities is one of the main challenges of context-aware computing. With the increasing number of built-in mobile sensors and other input sources, existing context models do not effectively handle context information related to personal user context. The objective of this research was to develop an improved context-aware model to support the context awareness needs of mobile applications. An existing context-aware model was selected as the most complete model to use as a basis for the proposed model to support context awareness in mobile applications. The existing context-aware model was modified to address the shortcomings of existing models in dealing with context information related to personal user context. The proposed model supports four different context dimensions, namely Physical, User Activity, Health and User Preferences. A prototype, called CoPro was developed, based on the proposed model, to demonstrate the effectiveness of the model. Several experiments were designed and conducted to determine if CoPro was effective, reliable and capable. CoPro was considered effective as it produced low-level context as well as inferred context. The reliability of the model was confirmed by evaluating CoPro using Quality of Context (QoC) metrics such as Accuracy, Freshness, Certainty and Completeness. CoPro was also found to be capable of dealing with the limitations of the mobile computing platform such as limited processing power. The research determined that the proposed context-aware model can be used to successfully support context awareness in mobile applications. Design recommendations were proposed and future work will involve converting the CoPro prototype into middleware in the form of an API to provide easier access to context awareness support in mobile applications

Context-aware Music Recommendation in the Car

Das Hören von Musik ist in unserer Gesellschaft zur wichtigsten Begleitaktivität geworden. Besonders das mobile und ubiquitäre Hören von Musik wurde in den letzten Jahren durch digitale Musikangebote sowie durch mobile Endgeräte wie MP3-Player oder Smartphones erweitert und vereinfacht. Die eigenen Musikbibliotheken werden zudem immer größer und stellen den Nutzer zunehmend vor Herausforderungen: Die Auswahl eines für die aktuelle Hörsituation passenden Musiktitels erweist sich als äußerst zeitaufwändig und erfordert zudem Interaktion mit dem System. Speziell beim Autofahren – einer der wichtigsten Hörsituationen von Musik – ist der Fahrer primär mit dem Führen des Fahrzeugs beschäftigt, dementsprechend können Musikempfehlungssysteme hier bei der Musikauswahl unterstützen. Die Berücksichtigung von Kontextparametern wie z.B. Umfeld, Straßenkategorie und Fahrtbelastung bei der Empfehlung kann dazu genutzt werden, besser auf Situationsänderungen zu reagieren. Diese speziellen Empfehlungssysteme werden als kontextorientierte Musikempfehlungssysteme bezeichnet. Ziel dieser Arbeit ist es, den Kontext im Fahrzeug in Bezug auf die Musikeinspielung von Kunden- und Kontextseite näher zu betrachten. Hierdurch sollen Ansätze identifiziert werden, wie die Musik im Fahrzeug an die situationsspezifischen Musikwünsche des Nutzers angepasst werden kann. Weiterhin wird der Fahrer in komplexen Fahrsituationen weniger gefordert. Dazu wird zunächst aufgezeigt, welche Möglichkeiten kontextorientierte Musikempfehlung bietet, wie sich die spezielle Situation des Autofahrens in Bezug auf das Hören von Musik darstellt und welche Ansätze bisherige Systeme bieten. Anschließend werden eigene Nutzerstudien zur kontextorientierten Musikeinspielung im Fahrzeug vorgestellt. Die Erkenntnisse aus Theorie, Praxis und eigenen Studien werden zusammengeführt und iterativ in einen Prototypen, der die Musik kontextorientiert einspielt, implementiert und evaluiert. Die Ergebnisse deuten darauf hin, dass sich durch die kontextorientierte Musikempfehlung im Fahrzeug in den drei Bereichen Fahrsicherheit, Fahrkomfort und Fahrtwahrnehmung Vorteile gegenüber der klassischen Musikeinspielung für den Autofahrer ergeben.Listening to music has become the most important accompanying activity in our society. Especially mobile and ubiquitous listening to music has been enhanced and simplified in recent years by digital music and mobile devices, such as MP3-players or smartphones. Additionally, the music libraries of the users are getting bigger, leading to new challenges the users have to face. For example, the selection of an appropriate song for the current listening situation proves to be extremely time-consuming and also requires an interaction with the system. Especially while driving, which is one of the most important listening situations, the driver is primarily engaged with driving. A music recommender system may assist in the process of music selection. The consideration of context parameters such as environment, type of road and driving load can be used in the recommendation process to better respond to situational changes. These particular recommender systems are referred to as context-aware music recommender systems. The aim of this work is to examine the music listening situation in the car from two perspectives: the customer and the context side. This is intended to identify possibilities how music playback in the car can be adapted to user situation-specific music requests. Further, the driver may benefit from less cognitive load in complex driving situations. For this purpose, it is shown which possibilities are offered by context-aware music recommendation, how the specific situation of driving affects the music listening behavior and which approaches are used by existing systems. Subsequently, conducted user studies for context-aware music playback in the car will be presented. Insights from theory, practice and conducted user studies are brought together and iteratively implemented into a context-aware music recommender prototype, which is then evaluated. Results suggest that context-aware music recommendation in the vehicle has particular advantages over classical music services when it comes to road safety, driving comfort and driving performance