Conference paper Open Access

The Many Faces of Users: Modeling Musical Preference

Eva Zangerle; Martin Pichl


DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="DOI">10.5281/zenodo.1492515</identifier>
  <creators>
    <creator>
      <creatorName>Eva Zangerle</creatorName>
    </creator>
    <creator>
      <creatorName>Martin Pichl</creatorName>
    </creator>
  </creators>
  <titles>
    <title>The Many Faces of Users: Modeling Musical Preference</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <dates>
    <date dateType="Issued">2018-09-23</date>
  </dates>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1492515</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.1492514</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/ismir</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">User models that capture the musical preferences of users are central for many tasks in music information retrieval and music recommendation, yet, it has not been fully explored and exploited. To this end, the musical preferences of users in the context of music recommender systems have mostly been captured in collaborative filtering-based approaches. Alternatively, users can be characterized by their average listening behavior and hence, by the mean values of a set of content descriptors of tracks the users listened to. However, a user may listen to highly different tracks and genres. Thus, computing the average of all tracks does not capture the user's listening behavior well. We argue that each user may have many different preferences that depend on contextual aspects (e.g., listening to classical music when working and hard rock when doing sports) and that user models should account for these different sets of preferences. In this paper, we provide a detailed analysis and evaluation of different user models that describe a user's musical preferences based on acoustic features of tracks the user has listened to.</description>
  </descriptions>
</resource>
145
81
views
downloads
All versions This version
Views 145145
Downloads 8181
Data volume 16.9 MB16.9 MB
Unique views 140140
Unique downloads 7474

Share

Cite as