802 research outputs found
Current Challenges and Visions in Music Recommender Systems Research
Music recommender systems (MRS) have experienced a boom in recent years,
thanks to the emergence and success of online streaming services, which
nowadays make available almost all music in the world at the user's fingertip.
While today's MRS considerably help users to find interesting music in these
huge catalogs, MRS research is still facing substantial challenges. In
particular when it comes to build, incorporate, and evaluate recommendation
strategies that integrate information beyond simple user--item interactions or
content-based descriptors, but dig deep into the very essence of listener
needs, preferences, and intentions, MRS research becomes a big endeavor and
related publications quite sparse.
The purpose of this trends and survey article is twofold. We first identify
and shed light on what we believe are the most pressing challenges MRS research
is facing, from both academic and industry perspectives. We review the state of
the art towards solving these challenges and discuss its limitations. Second,
we detail possible future directions and visions we contemplate for the further
evolution of the field. The article should therefore serve two purposes: giving
the interested reader an overview of current challenges in MRS research and
providing guidance for young researchers by identifying interesting, yet
under-researched, directions in the field
DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation
In recent years, there has been growing focus on the study of automated
recommender systems. Music recommendation systems serve as a prominent domain
for such works, both from an academic and a commercial perspective. A
fundamental aspect of music perception is that music is experienced in temporal
context and in sequence. In this work we present DJ-MC, a novel
reinforcement-learning framework for music recommendation that does not
recommend songs individually but rather song sequences, or playlists, based on
a model of preferences for both songs and song transitions. The model is
learned online and is uniquely adapted for each listener. To reduce exploration
time, DJ-MC exploits user feedback to initialize a model, which it subsequently
updates by reinforcement. We evaluate our framework with human participants
using both real song and playlist data. Our results indicate that DJ-MC's
ability to recommend sequences of songs provides a significant improvement over
more straightforward approaches, which do not take transitions into account.Comment: -Updated to the most recent and completed version (to be presented at
AAMAS 2015) -Updated author list. in Autonomous Agents and Multiagent Systems
(AAMAS) 2015, Istanbul, Turkey, May 201
Geolocation Adaptive Music Player
date-added: 2017-12-22 20:02:39 +0000 date-modified: 2017-12-22 20:05:50 +0000 keywords: adaptive music, intelligent music player, semantic audio, feature extraction bdsk-url-1: https://smartech.gatech.edu/bitstream/handle/1853/54586/WAC2016-47.pdfdate-added: 2017-12-22 20:02:39 +0000 date-modified: 2017-12-22 20:05:50 +0000 keywords: adaptive music, intelligent music player, semantic audio, feature extraction bdsk-url-1: https://smartech.gatech.edu/bitstream/handle/1853/54586/WAC2016-47.pdfWe present a web-based cross-platform adaptive music player that combines music information retrieval (MIR) and audio processing technologies with the interaction capabilities offered by GPS-equipped mobile devices. The application plays back a list of music tracks, which are linked to geographic paths in a map. The music player has two main enhanced features that adjust to the location of the user, namely, adaptable length of the songs and automatic transitions between tracks. Music tracks are represented as data packages containing audio and metadata (descriptive and behavioral) that builds on the concept of Digital Music Object (DMO). This representation, in line with nextgeneration web technologies, allows for exible production and consumption of novel musical experiences. A content provider assembles a data pack with music, descriptive analysis and action parameters that users can experience and control within the restrictions and templates defined by the provider
A Content-Aware Interactive Explorer of Digital Music Collections: The Phonos Music Explorer
La tesi si propone di utilizzare le più recenti tecnologie del Music Information Retrieval (MIR) al fine di creare un esploratore interattivo di cataloghi musicali. Il software utilizza tecniche avanzate quali riduzione di dimensionalità  mediante FastMap, generazione e streaming over-the-network di contenuto audio, segmentazione e estrazione di descrittori da segnali audio. Inoltre, il software è in grado di adattare in real-time il proprio output sulla base di interazioni dell'utent
Recommending media content based on machine learning methods
Dissertação para obtenção do Grau de Mestre em
Engenharia InformáticaInformation is nowadays made available and consumed faster than ever before. This information technology generation has access to a tremendous deal of data and is left with
the heavy burden of choosing what is relevant. With the increasing growth of media
sources, the amount of content made available to users has become overwhelming and in need to be managed. Recommender systems emerged with the purpose of providing
personalized and meaningful content recommendations based on users’ preferences and usage history. Due to their utility and commercial potential, recommender systems integrate many audiovisual content providers and represent one of their most important and
valuable services. The goal of this thesis is to develop a recommender system based on
matrix factorization methods, capable of providing meaningful and personalized product
recommendations to individual users and groups of users, by taking into account
users’ rating patterns and biased tendencies, as well as their fluctuations throughout time
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