Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Inferring similarity between music objects with application to playlist generation
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
A Large-Scale Evaluation of Acoustic and Subjective Music-Similarity Measures
Computer Music Journal
Personal vs. commercial content: the similarities between consumer use of photos and music
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An innovative three-dimensional user interface for exploring music collections enriched
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Exploring music collections on mobile devices
Proceedings of the 10th international conference on Human computer interaction with mobile devices and services
Map-based music interfaces for mobile devices
MM '08 Proceedings of the 16th ACM international conference on Multimedia
From Web to Map: Exploring the World of Music
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Music Recommendation Mapping and Interface Based on Structural Network Entropy
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Visually and Acoustically Exploring the High-Dimensional Space of Music
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Audio genre classification using percussive pattern clustering combined with timbral features
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Colorizing tags in tag cloud: a novel query-by-tag music search system
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Going Through the Clouds: Search Overviews and Browsing of Movies
Proceeding of the 16th International Academic MindTrek Conference
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The size of personal music collections has constantly increased over the past years. As a result, the traditional metadata based lists to browse these collections have reached their limits. Interfaces that are based on music similarity offer an alternative and thus are increasingly gaining attention. Music similarity is typically either derived from audio-features (objective approach) or from user driven information sources, such as collaborative filtering or social tags (subjective approach). Studies show that the latter techniques outperform audio-based approaches when it comes to describe the perceived music similarity. However, subjective approaches typically only define pairwise relations as opposed to the global notion of similarity given by audio-feature spaces. Many of the proposed interfaces for similarity based music access inherently depend on this global notion and are thus not applicable to user driven music similarity measures. The first contribution of this paper is a high dimensional music space that is based on user driven similarity measures. It combines the advantages of audio-feature spaces (global view) with the advantages of subjective sources that better reflect the users' perception. The proposed space compactly represents similarity and therefore is well suited for offline use, such as in mobile applications. To demonstrate the practical applicability, the second contribution is a comprehensive mobile music player that incorporates several smart interfaces to access the user's music collection. Based on this application, we finally present a large-scale user study that underlines the benefits of the introduced interfaces and shows their great user acceptance.