443 research outputs found

    HOW HAVE MUSICIANS’ CAREERS CHANGED IN THE DIGITAL PLATFORM ERA?

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    openLa "platform society" Ăš una realtĂ . O meglio, una seconda realtĂ  che si aggiunge e si sovrappone alla dimensione fisica. CiĂČ vale anche per l'industria musicale. Gli ulitmi quindici anni hanno visto la crescita di piattaforme di streaming musicale come Spotify, con il conseguente riposizionamento dei vari attori coinvolti nell'industria e del relativo sistema economico. Per i musicisti, il nuovo scenario apre a nuove possibilitĂ  e pone nuove sfide.The platform society is a reality. Better said, it is a second reality that adds and intertwines with the physical dimension. This is so also for the music industry. The last fifteen years have seen the growth of music streaming platforms such as Spotify and the consequent repositioning of actors and revolution of the music economy. The new scenario opens up new possibilities and poses new challenges to musicians' careers

    A methodology for contextual recommendation using artificial neural networks

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    “A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Doctor of Philosophy”.Recommender systems are an advanced form of software applications, more specifically decision-support systems, that efficiently assist the users in finding items of their interest. Recommender systems have been applied to many domains from music to e-commerce, movies to software services delivery and tourism to news by exploiting available information to predict and provide recommendations to end user. The suggestions generated by recommender systems tend to narrow down the list of items which a user may overlook due to the huge variety of similar items or users’ lack of experience in the particular domain of interest. While the performance of traditional recommender systems, which rely on relatively simpler information such as content and users’ filters, is widely accepted, their predictive capability perfomrs poorly when local context of the user and situated actions have significant role in the final decision. Therefore, acceptance and incorporation of context of the user as a significant feature and development of recommender systems utilising the premise becomes an active area of research requiring further investigation of the underlying algorithms and methodology. This thesis focuses on categorisation of contextual and non-contextual features within the domain of context-aware recommender system and their respective evaluation. Further, application of the Multilayer Perceptron Model (MLP) for generating predictions and ratings from the contextual and non-contextual features for contextual recommendations is presented with support from relevant literature and empirical evaluation. An evaluation of specifically employing artificial neural networks (ANNs) in the proposed methodology is also presented. The work emphasizes on both algorithms and methodology with three points of consideration:\ud contextual features and ratings of particular items/movies are exploited in several representations to improve the accuracy of recommendation process using artificial neural networks (ANNs), context features are combined with user-features to further improve the accuracy of a context-aware recommender system and lastly, a combination of the item/movie features are investigated within the recommendation process. The proposed approach is evaluated on the LDOS-CoMoDa dataset and the results are compared with state-of-the-art approaches from relevant published literature

    Design revolutions: IASDR 2019 Conference Proceedings. Volume 3: People

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    In September 2019 Manchester School of Art at Manchester Metropolitan University was honoured to host the bi-annual conference of the International Association of Societies of Design Research (IASDR) under the unifying theme of DESIGN REVOLUTIONS. This was the first time the conference had been held in the UK. Through key research themes across nine conference tracks – Change, Learning, Living, Making, People, Technology, Thinking, Value and Voices – the conference opened up compelling, meaningful and radical dialogue of the role of design in addressing societal and organisational challenges. This Volume 3 includes papers from People track of the conference

    What If Your Car Would Care? Exploring Use Cases For Affective Automotive User Interfaces

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    In this paper we present use cases for affective user interfaces (UIs) in cars and how they are perceived by potential users in China and Germany. Emotion-aware interaction is enabled by the improvement of ubiquitous sensing methods and provides potential benefits for both traffic safety and personal well-being. To promote the adoption of affective interaction at an international scale, we developed 20 mobile in-car use cases through an inter-cultural design approach and evaluated them with 65 drivers in Germany and China. Our data shows perceived benefits in specific areas of pragmatic quality as well as cultural differences, especially for socially interactive use cases. We also discuss general implications for future affective automotive UI. Our results provide a perspective on cultural peculiarities and a concrete starting point for practitioners and researchers working on emotion-aware interfaces

    Temporal Feature Integration for Music Organisation

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    Humanist evaluation methods in locative media design

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    Media design can be used for research purposes if it includes a clearly defined research question, and clear evaluation to see whether an answer to the research question has been found. Using a project with locative media for classical music communication as our example, we discuss common evaluation methods from the User Experience field, observing that they all tend to test “interface” and not “content.” Instead we propose three other methods of evaluation, that have a basis in humanist theories, such as textual analysis and genre studies: (1) Qualitative interviews with evaluators after the evaluation, asking them to describe the service in their own words, followed by a semantic analysis to get at how they have understood the service. (2) Within-subject A/B tests with alternative versions that are different in key aspects. (3) Peer review by experienced design researchers, who are likely to have a more fine-tuned vocabulary to express their opinions

    The utilitarian and hedonic outcomes of music information seeking in everyday life

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    This qualitative study focuses on what contributes to making a music information-seeking experience satisfying in the context of everyday life. Data were collected through in-depth interviews conducted with 15 younger adults (18 to 29 years old). The analysis revealed that satisfaction could depend on both hedonic (i.e., experiencing pleasure) and utilitarian outcomes. It was found that two types of utilitarian outcomes contributed to satisfaction: (1) the acquisition of music, and (2) the acquisition of information about music. Information about music was gathered to (1) enrich the listening experience, (2) increase one's music knowledge, and/or (3) optimize future acquisition. This study contributes to a better understanding of music information-seeking behavior in recreational contexts. It also has implications for music information retrieval systems design: results suggest that these systems should be engaging, include a wealth of extra-musical information, allow users to navigate among music items, and encourage serendipitous encountering of music.Social Sciences and Humanities Research Council of Canada; Andrew W. Mellon Foundatio

    Context-Aware Recommendation Systems in Mobile Environments

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    Nowadays, the huge amount of information available may easily overwhelm users when they need to take a decision that involves choosing among several options. As a solution to this problem, Recommendation Systems (RS) have emerged to offer relevant items to users. The main goal of these systems is to recommend certain items based on user preferences. Unfortunately, traditional recommendation systems do not consider the user’s context as an important dimension to ensure high-quality recommendations. Motivated by the need to incorporate contextual information during the recommendation process, Context-Aware Recommendation Systems (CARS) have emerged. However, these recent recommendation systems are not designed with mobile users in mind, where the context and the movements of the users and items may be important factors to consider when deciding which items should be recommended. Therefore, context-aware recommendation models should be able to effectively and efficiently exploit the dynamic context of the mobile user in order to offer her/him suitable recommendations and keep them up-to-date.The research area of this thesis belongs to the fields of context-aware recommendation systems and mobile computing. We focus on the following scientific problem: how could we facilitate the development of context-aware recommendation systems in mobile environments to provide users with relevant recommendations? This work is motivated by the lack of generic and flexible context-aware recommendation frameworks that consider aspects related to mobile users and mobile computing. In order to solve the identified problem, we pursue the following general goal: the design and implementation of a context-aware recommendation framework for mobile computing environments that facilitates the development of context-aware recommendation applications for mobile users. In the thesis, we contribute to bridge the gap not only between recommendation systems and context-aware computing, but also between CARS and mobile computing.<br /
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