Automatic Music Playlist Generation via Simulation-based Reinforcement Learning

Personalization of playlists is a common feature in music streaming services, but conventional techniques, such as collaborative filtering, rely on explicit assumptions regarding content quality to learn how to make recommendations. Such assumptions often result in misalignment between offline model objectives and online user satisfaction metrics. In this paper, we present a reinforcement learning framework that solves for such limitations by directly optimizing for user satisfaction metrics via the use of a simulated playlist-generation environment. Using this simulator we develop and train a modified Deep Q-Network, the action head DQN (AH-DQN), in a manner that addresses the challenges imposed by the large state and action space of our RL formulation. The resulting policy is capable of making recommendations from large and dynamic sets of candidate items with the expectation of maximizing consumption metrics. We analyze and evaluate agents offline via simulations that use environment models trained on both public and proprietary streaming datasets. We show how these agents lead to better user-satisfaction metrics compared to baseline methods during online A/B tests. Finally, we demonstrate that performance assessments produced from our simulator are strongly correlated with observed online metric results.Comment: 10 pages. KDD 2

Student-Centered Learning: Functional Requirements for Integrated Systems to Optimize Learning

The realities of the 21st-century learner require that schools and educators fundamentally change their practice. "Educators must produce college- and career-ready graduates that reflect the future these students will face. And, they must facilitate learning through means that align with the defining attributes of this generation of learners."Today, we know more than ever about how students learn, acknowledging that the process isn't the same for every student and doesn't remain the same for each individual, depending upon maturation and the content being learned. We know that students want to progress at a pace that allows them to master new concepts and skills, to access a variety of resources, to receive timely feedback on their progress, to demonstrate their knowledge in multiple ways and to get direction, support and feedback from—as well as collaborate with—experts, teachers, tutors and other students.The result is a growing demand for student-centered, transformative digital learning using competency education as an underpinning.iNACOL released this paper to illustrate the technical requirements and functionalities that learning management systems need to shift toward student-centered instructional models. This comprehensive framework will help districts and schools determine what systems to use and integrate as they being their journey toward student-centered learning, as well as how systems integration aligns with their organizational vision, educational goals and strategic plans.Educators can use this report to optimize student learning and promote innovation in their own student-centered learning environments. The report will help school leaders understand the complex technologies needed to optimize personalized learning and how to use data and analytics to improve practices, and can assist technology leaders in re-engineering systems to support the key nuances of student-centered learning

An Interactive System for Generating Music from Moving Images

Moving images contain a wealth of information pertaining to motion. Motivated by the interconnectedness of music and movement, we present a framework for transforming the kinetic qualities of moving images into music. We developed an interactive software system that takes video as input and maps its motion attributes into the musical dimension based on perceptually grounded principles. The system combines existing sonification frameworks with theories and techniques of generative music. To evaluate the system, we conducted a two-part experiment. First, we asked participants to make judgements on video-audio correspondence from clips generated by the system. Second, we asked participants to give ratings for audiovisual works created using the system. These experiments revealed that 1) the system is able to generate music with a significant level of perceptual correspondence to the source video’s motion and 2) the system can effectively be used as an artistic tool for generative composition

Revenue models in the context of online digital audio companies: Making an optimal choice between advertising and paid subscriptions

Objectives of the Study This study sets out to examine the digital Internet audio market through eight case companies and their business models, and determine whether future entrants into the market should focus their monetization efforts on advertising-based models or subscription-based models. The main objective of the study is to provide an educated guess on which revenue models future entrants should emphasize based on the current situation in the market today. Academic background and methodology The study is based on research into business models, targeted and mobile advertising, and winner-take-all market characteristics in platform industries. A widely used business model evaluation framework is described and used to assess the selected case companies to examine the current characteristics of players in the market in detail. The results of the empirical study are then used as a basis for formulating key findings about the market and to formulate a recommendation for future market entrants concerning their potential choice of revenue model and value proposition. Findings and conclusions The study finds that digital Internet audio companies are roughly divided into two camps: subscription-based companies offering on-demand music and advertising-based companies offering streaming audio in various different collections of feature sets. Despite many negative arguments against selling advertising, the study finds that it is still a smarter market to enter into given the winner-take-all tendencies of the subscription-based market and the significant funding incumbents are competing with against each other already. Future avenues for research are opened in studying whether a winner-take-all market truly does emerge in subscription-based online music, in how strongly Internet audio advertising ends up growing, and how a revenue model is determined and then paired with a logical value proposition that fits it

Biosignal controlled recommendation in entertainment systems

With the explosive growth of the entertainment contents and the ubiquitous access of them via fixed or mobile computing devices, recommendation systems become essential tools to help the user to find the right entertainment at the right time and location. I envision that by integrating the bio signal input into the recommendation process, it will help the users not only to find interesting contents, but also to increase one’s comfort level by taking into account the biosginal feedback from the users. The goal of this project was to develop a biosignal controlled entertainment recommendation system that increases the user’s comfort level by reducing the level of stress. As the starting point, this project aims to contribute to the field of recommendation systems with two points. The first is the mechanism of embedding the biosignal non-intrusively into the recommendation process. The second is the strategy of the biosignal controlled recommendation to reduce stress. Heart rate controlled in-flight music recommendation is chosen as its application domain. The hypothesis of this application is that, the passenger's heart rate deviates from the normal due to unusual long haul flight cabin environment. By properly designing a music recommendation system to recommend heart rate controlled personalized music playlists to the passenger, the passengers' heart rate can be uplifted, down-lifted back to normal or kept within normal, thus their stress can be reduced. Four research questions have been formulated based on this hypothesis. After the literature study, the project went mainly through three phases: framework design, system implementation and user evaluation to answer these research questions. During the framework design phase, the heart rate was firstly modeled as the states of bradycardia, normal and tachycardia. The objective of the framework is that, if the user's heart rate is higher or lower than the normal heart rate, the system recommends a personalized music playlist accordingly to transfer the user’s heart rate back to normal, otherwise to keep it at normal. The adaptive framework integrates the concepts of context adaptive systems, user profiling, and the methods of using music to adjust the heart rate in a feedback control system. In the feedback loop, the playlists were composed using a Markov decision process. Yet, the framework allows the user to reject the recommendations and to manually select the favorite music items. During this process, the system logs the interactions between the user and the system for later learning the user’s latest music preferences. The designed framework was then implemented with platform independent software architecture. The architecture has five abstraction levels. The lowest resource level contains the music source, the heart rate sensors and the user profile information. The second layer is for resource management. In this layer are the manager components to manage the resources from the first layer and to modulate the access from upper layers to these resources. The third layer is the database, acting as a data repository. The fourth layer is for the adaptive control, which includes the user feedback log, the inference engine and the preference learning component. The top layer is the user interface. In this architecture, the layers and the components in the layers are loosely coupled, which ensures the flexibility. The implemented system was used in the user experiments to validate the hypothesis. The experiments simulated the long haul flights from Amsterdam to Shanghai with the same time schedule as the KLM flights. Twelve subjects were invited to participate in the experiments. Six were allocated to the controlled group and others were allocated to the treatment group. In addition to a normal entertainment system for the control group, the treatment group was also provided with the heart rate controlled music recommendation system. The experiments results validated the hypothesis and answered the research questions. The passenger's heart rate deviates from normal. In our user experiments, the passenger's heart rate was in the bradycardia state 24.6% of time and was in the tachycardia state 7.3% of time. The recommended uplifting music reduces the average bradycardia state duration from 14.78 seconds in the control group to 6.86 seconds in the treatment group. The recommended keeping music increases the average normal state duration from 24.66 seconds in the control group to 29.79 seconds in the treatment group. The recommended down-lifting music reduces the average tachycardia state duration from 13.89 seconds in the control group to 6.53 seconds in the treatment group. Compared to the control group, the stress of the treatment group has been reduced significantly

Exploratory Browsing

In recent years the digital media has influenced many areas of our life. The transition from analogue to digital has substantially changed our ways of dealing with media collections. Today‟s interfaces for managing digital media mainly offer fixed linear models corresponding to the underlying technical concepts (folders, events, albums, etc.), or the metaphors borrowed from the analogue counterparts (e.g., stacks, film rolls). However, people‟s mental interpretations of their media collections often go beyond the scope of linear scan. Besides explicit search with specific goals, current interfaces can not sufficiently support the explorative and often non-linear behavior. This dissertation presents an exploration of interface design to enhance the browsing experience with media collections. The main outcome of this thesis is a new model of Exploratory Browsing to guide the design of interfaces to support the full range of browsing activities, especially the Exploratory Browsing. We define Exploratory Browsing as the behavior when the user is uncertain about her or his targets and needs to discover areas of interest (exploratory), in which she or he can explore in detail and possibly find some acceptable items (browsing). According to the browsing objectives, we group browsing activities into three categories: Search Browsing, General Purpose Browsing and Serendipitous Browsing. In the context of this thesis, Exploratory Browsing refers to the latter two browsing activities, which goes beyond explicit search with specific objectives. We systematically explore the design space of interfaces to support the Exploratory Browsing experience. Applying the methodology of User-Centered Design, we develop eight prototypes, covering two main usage contexts of browsing with personal collections and in online communities. The main studied media types are photographs and music. The main contribution of this thesis lies in deepening the understanding of how people‟s exploratory behavior has an impact on the interface design. This thesis contributes to the field of interface design for media collections in several aspects. With the goal to inform the interface design to support the Exploratory Browsing experience with media collections, we present a model of Exploratory Browsing, covering the full range of exploratory activities around media collections. We investigate this model in different usage contexts and develop eight prototypes. The substantial implications gathered during the development and evaluation of these prototypes inform the further refinement of our model: We uncover the underlying transitional relations between browsing activities and discover several stimulators to encourage a fluid and effective activity transition. Based on this model, we propose a catalogue of general interface characteristics, and employ this catalogue as criteria to analyze the effectiveness of our prototypes. We also present several general suggestions for designing interfaces for media collections

Parsing consumption preferences of music streaming audiences

As demands for insights on music streaming listeners continue to grow, scientists and industry analysts face the challenge to comprehend a mutated consumption behavior, which demands a renewed approach to listener typologies. This study aims to determine how audience segmentation can be performed in a time-relevant and replicable manner. Thus, it interrogates which parameters best serve as indicators of preferences to ultimately assist in delimiting listener segments. Accordingly, the primary objective of this research is to develop a revised typology that classifies music streaming listeners in the light of the progressive phenomenology of music listening. The hypothesis assumes that this could be solved by positioning listeners – rather than products – at the center of streaming analysis and supplementing sales- with user-centered metrics. The empirical research of this paper was based on grounded theories, enriched by analytical case studies. For this purpose, behavioral and psychological research results were interconnected with market analysis and streaming platform usage data. Analysis of the results demonstrates that a concatenation of multi-dimensional data streams facilitates the derivation of a typology that is applicable to varying audience pools. The findings indicate that for the delimitation of listener types, the motivation, and listening context are essential key constituents. Since these variables demand insights that reach beyond existing metrics, descriptive data points relating to the listening process are subjoined. Ultimately, parameter indexation results in listener profiles that offer novel access points for investigations, which make imperceptible, interdisciplinary correlations tangible. The framework of the typology can be consulted in analytical and creational processes. In this respect, the results of the derived analytical approach contribute to better determine and ultimately satisfy listener preferences.Während die Nachfrage nach Erkenntnissen über Musik-Streaming-Hörer kontinuierlich steigt, stehen Wissenschaftler sowie Industrieanalysten einem geänderten Konsumptions- verhalten gegenüber, das eine überarbeitete Hörertypologie fordert. Die vorliegende Studie erörtert, wie eine Hörersegmentierung auf zeitgemäße und replizierbare Weise umgesetzt werden kann. Demnach beschäftigt sie sich mit der Frage, welche Parameter am besten als Indikatoren für Hörerpräferenzen dienen und wie diese zur Abgrenzung der Publikumsseg- mente beitragen können. Dementsprechend ist es das primäre Ziel dieser Forschung, eine überarbeitete Typologie aufzustellen, die Musik-Streaming-Hörer in Anbetracht der progressiven Erscheinungsform des Musikhörens klassifiziert. Die Hypothese nimmt an, dass dies realisierbar ist, wenn der Hörer – anstelle von Produkten – im Zentrum der Streaming-Analyse steht und absatzzen- trierte durch hörerzentrierte Messungen ergänzt werden. Die empirische Forschung basiert auf systematischen Theorien, untermauert durch analytische Fallbeispiele. Hierfür werden psychologische und verhaltenswissenschaftliche Forschungserkenntnisse mit Marktanalysen und Nutzerdaten von Musikstreaming-Portalen fusioniert. Die Analyse der Ergebnisse verdeutlicht, dass eine Verkettung von multidimensionalen Rohdaten die Erhebung einer Typologie ermöglicht, die auf mehrere Hörergruppen anwend- bar ist. Die Befunde signalisieren, dass die Hörmotivation und der Hörkontext bei der Abgrenzung der Publikumstypen Schlüsselelemente darstellen. Da diese Variablen spezifis- che Kenntnisse fordern, die über vorliegende Kennzahlen hinausgehen, werden deskriptive Datenpunkte über den Hörvorgang ergänzt. Letztlich, resultiert die Indexierung der Pa- rameter in Hörerprofilen, die neue Zugangspunkte für Untersuchungen bieten, die nicht ersichtliche, interdisziplinäre Korrelationen greifbar machen. Das Gerüst der Hörertypologie kann sowohl in Erstellungs- als auch in Analyseprozessen herangezogen werden. Somit tragen die Ergebnisse der entwickelten Analysemethode zum Verständnis und letztlich zur Erfüllung von Hörerpräferenzen bei