Harvesting and Structuring Social Data in Music Information Retrieval

Abstract. An exponentially growing amount of music and sound resources are being shared by communities of users on the Internet. Social media content can be found with different levels of structuring, and the contributing users might be experts or non-experts of the domain. Harvesting and structuring this information semantically would be very useful in context-aware Music Information Retrieval (MIR). Until now, scant research in this field has taken advantage of the use of formal knowledge representations in the process of structuring information. We propose a methodology that combines Social Media Mining, Knowledge Extraction and Natural Language Processing techniques, to extract meaningful context information from social data. By using the extracted information we aim to improve retrieval, discovery and annotation of music and sound resources. We define three different scenarios to test and develop our methodology

Just-for-Me: An Adaptive Personalization System for Location-Aware Social Music Recommendation

The fast growth of online communities and increasing pop-ularity of internet-accessing smart devices have significantly changed the way people consume and share music. As an emerging technology to facilitate effective music retrieval on the move, intelligent recommendation has been recently re-ceived great attentions in recent years. While a large amount of efforts have been invested in the field, the technology is still in its infancy. One of the major reasons for this stagna-tion is due to inability of the existing approaches to compre-hensively take multiple kinds of contextual information into account. In the paper, we present a novel recommender sys-tem called Just-for-Me to facilitate effective social music rec-ommendation by considering users ’ location related contexts as well as global music popularity trends. We also develop an unified recommendation model to integrate the contex-tual factors as well as music contents simultaneously. Fur-thermore, pseudo-observations are proposed to overcome the cold-start and sparsity problems. An extensive experimental study based on different test collections demonstrates that Just-for-Me system can significantly improve the recommen-dation performance at various geo-locations

On effective location-aware music recommendation

Ministry of Education, Singapore under its Academic Research Funding Tier

Effects of recommendations on the playlist creation behavior of users

International audienceThe digitization of music, the emergence of online streaming platforms and mobile apps have dramatically changed the ways we consume music. Today, much of the music that we listen to is organized in some form of a playlist, and many users of modern music platforms create playlists for themselves or to share them with others. The manual creation of such playlists can however be demanding, in particular due to the huge amount of possible tracks that are available online. To help users in this task, music platforms like Spotify provide users with interactive tools for playlist creation. These tools usually recommend additional songs to include given a playlist title or some initial tracks. Interestingly, little is known so far about the effects of providing such a recommendation functionality. We therefore conducted a user study involving 270 subjects, where one half of the participants-the treatment group-were provided with automated recommendations when performing a playlist construction task. We then analyzed to what extent such recommendations are adopted by users and how they influence their choices. Our results, among other aspects, show that about two thirds of the treatment group made active use of the recommendations. Further analyses provide additional insights about the underlying reasons why users selected certain recommendations. Finally, our study also reveals that the mere presence of the recommendations impacts the choices of the participants, even in cases when none of the recommendations was actually chosen

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