Music Information Retrieval: An Inspirational Guide to Transfer from Related Disciplines

The emerging field of Music Information Retrieval (MIR) has been influenced by neighboring domains in signal processing and machine learning, including automatic speech recognition, image processing and text information retrieval. In this contribution, we start with concrete examples for methodology transfer between speech and music processing, oriented on the building blocks of pattern recognition: preprocessing, feature extraction, and classification/decoding. We then assume a higher level viewpoint when describing sources of mutual inspiration derived from text and image information retrieval. We conclude that dealing with the peculiarities of music in MIR research has contributed to advancing the state-of-the-art in other fields, and that many future challenges in MIR are strikingly similar to those that other research areas have been facing

Interactive Latent Space for Mood-Based Music Recommendation

The way we listen to music has been changing fundamentally in past two decades with the increasing availability of digital recordings and portability of music players. Up to date research in music recommendation attracted millions of users to online, music streaming services, containing tens of millions of tracks (e.g. Spotify, Pandora). The main focus of up to date research in recommender systems has been algorithmic accuracy and optimization of ranking metrics. However, recent work has highlighted the importance of other aspects of the recommendation process, including explanation, transparency, control and user experience in general. Building on these aspects, this dissertation explores user interaction, control and visual explanation of music related mood metadata during recommendation process. It introduces a hybrid recommender system that suggests music artists by combining mood-based and audio content filtering in a novel interactive interface. The main vehicle for exploration and discovery in music collection is a novel visualization that maps moods and artists in the same, latent space, built upon reduced dimensions of high-dimensional artist-mood associations. It is not known what the reduced dimensions represent and this work uses hierarchical mood model to explain the constructed space. Results of two user studies, with over 200 participants each, show that visualization and interaction in a latent space improves acceptance and understanding of both metadata and item recommendations. However, too much of either can result in cognitive overload and a negative impact on user experience. The proposed visual mood space and interactive features, along with the aforementioned findings, aim to inform design of future interactive recommendation systems

THREE ESSAYS ON COMPETITIVE STRATEGIES FOR DIGITAL PLATFORM BUSINESSES

As businesses in many industries adopt the platform business model, many aspects of the traditional business are going through a shake-up, including competition and strategies for gaining competitive advantage. When platforms are competing with each other, the network effects due to having a strong installed base create a strategic advantage and shape the competition. Additionally, another level of competition in the world of platforms is between complementors on a given platform which is also influenced by the presence of the network effects. In the three studies of this dissertation, we focus on competitive strategies for digital platform businesses. In the first essay, we look at competition between platforms and examine the emergence of Winners-Take-Some (WTS) market outcome in IT platform markets, where such markets are expected to yield a Winner-Takes-All (WTA) outcome. We use the cyclical video game console market as an appropriate context to investigate the influential factors in the market outcome in platform markets. We find a consistent increase in multi-homing among the most popular video-games that can pave the way for the emergence of WTS outcome. In the second essay, we are turning our focus to the strategies that platforms can adopt to improve emerging success metrics such as user engagement. We examine how digital content platforms can improve users’ engagement by providing popularity information signals. We evaluate the effect of conflicting and aligned information signals on users’ engagement in the context of music content platforms. We find that conflicting popularity information signals are more effective in increasing user engagement than the aligned popularity information signals. In the third essay and in the context of mobile app platforms, we focus on the competition between complementors. We study the role of app category characteristics on the performance of mobile app developers who offer apps in those categories and strive to gain competitive advantage. We evaluate category concentration and category popularity as two important factors and find that respectively, they negatively and positively influence new app’s performance for a given developer. We find that the negative effect of category concentration is stronger than the positive effect of category popularity