1,074 research outputs found
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
Design and Evaluation of a Probabilistic Music Projection Interface
We describe the design and evaluation of a probabilistic
interface for music exploration and casual playlist generation.
Predicted subjective features, such as mood and
genre, inferred from low-level audio features create a 34-
dimensional feature space. We use a nonlinear dimensionality
reduction algorithm to create 2D music maps of
tracks, and augment these with visualisations of probabilistic
mappings of selected features and their uncertainty.
We evaluated the system in a longitudinal trial in users’
homes over several weeks. Users said they had fun with the
interface and liked the casual nature of the playlist generation.
Users preferred to generate playlists from a local
neighbourhood of the map, rather than from a trajectory,
using neighbourhood selection more than three times more
often than path selection. Probabilistic highlighting of subjective
features led to more focused exploration in mouse
activity logs, and 6 of 8 users said they preferred the probabilistic
highlighting mode
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On Birthing Dancing Stars: The Need for Bounded Chaos in Information Interaction
While computers causing chaos is acommon social trope, nearly the entirety of the history of computing is dedicated to generating order. Typical interactive information retrieval tasks ask computers to support the traversal and exploration of large, complex information spaces. The implicit assumption is that they are to support users in simplifying the complexity (i.e. in creating order from chaos). But for some types of task, particularly those that involve the creative application or synthesis of knowledge or the creation of new knowledge, this assumption may be incorrect. It is increasingly evident that perfect order—and the systems we create with it—support highly-structured information tasks well, but provide poor support for less-structured tasks.We need digital information environments that help create a little more chaos from order to spark creative thinking and knowledge creation. This paper argues for the need for information systems that offerwhat we term ‘bounded chaos’, and offers research directions that may support the creation of such interface
A multicriteria ant colony algorithm for generating music playlists
In this paper we address the problem of music playlist generation based on the user-personalized specification
of context information. We propose a generic semantic multicriteria ant colony algorithm capable
of dealing with domain-specific problems by the use of ontologies. It also employs any associated
metadata defined in the search space to feed its solution-building process and considers any restrictions
the user may have specified. An example is given of the use of the algorithm for the problem of automatic
generation of music playlists, some experimental results are presented and the behavior of the approach
is explained in different situations.
2011 Elsevier Ltd. All rights reserved.This work has been partially supported by the Spanish Ministry of Education and Science under the funding project CENIT-MIOI CENIT-2008 1019 and by the Microsoft Research Labs (Cambridge) under the "Create, Play and Learn" program.Mocholi Agües, JA.; Martinez Valero, VM.; Jaén Martínez, FJ.; Catalá Bolós, A. (2012). A multicriteria ant colony algorithm for generating music playlists. Expert Systems with Applications. 39(3):2270-2278. doi:10.1016/j.eswa.2011.07.131S2270227839
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