• Publications
  • Influence
Music Recommendation and Discovery - The Long Tail, Long Fail, and Long Play in the Digital Music Space
TLDR
This book describes how music recommenders work, explores some of the limitations seen in current recommenders, offers techniques for evaluating the effectiveness of music recommendations and demonstrates how to build effective recommenders by offering two real-world recommender examples.
Music Recommendation and Discovery in the Long Tail
TLDR
The Long Tail curve of artist popularity is model to predict —potentially— interesting and unknown music, hidden in the tail of the popularity curve, which has significant implications for recommender systems that assist users to explore the Long Tail, digging for content they might like.
A new approach to evaluating novel recommendations
TLDR
A user-centric experiment shows that even though a social-based approach recommends less novel items than the authors' CB, users' perceived quality is better than those recommended by a pure CB method.
Foafing the Music: Bridging the Semantic Gap in Music Recommendation
TLDR
An overview of the Foafing the Music system, which uses the Friend of a Friend and RDF Site Summary vocabularies for recommending music to a user, depending on the user's musical tastes and listening habits, is given.
From hits to niches?: or how popular artists can bias music recommendation and discovery
TLDR
The results from the experiments reveal that---as expected by its inherent social component---the collaborative filtering approach is prone to popularity bias, which has some consequences on the discovery ratio as well as in the navigation through the Long Tail.
Semantic Integration and Retrieval of Multimedia Metadata
TLDR
This work has used the generated MPEG-7 OWL ontology as an “upper-ontology” for multimedia metadata, where three different music schemas have been linked and it has been possible to retrieve related information from instances of all the metadata sources.
Multimedia Vocabularies on the Semantic Web
TLDR
This document gives an overview on the state-of-the-art of multimedia metadata formats and the integration of the multimedia vocabularies into the Semantic Web.
Music recommendation and discovery revisited
TLDR
This tutorial looks at the current state of the art in music recommendation and discovery, and examines current commercial and research systems, focusing on the advantages and the disadvantages of the various recommendation strategies.
The Quest for Musical Genres: Do the Experts and the Wisdom of Crowds Agree?
TLDR
A multi–faceted approach for musical genre using expert based classifications, dynamic associations derived from the wisdom of crowds, and content–based analysis can improve genre classification, as well as other relevant MIR tasks such as music similarity or music recommendation.
Annotating Music Collections: How Content-Based Similarity Helps to Propagate Labels
TLDR
The main goal of the work is to ease the process of annotating huge music collections, by using content-based similarity distances as a way to propagate labels among songs.
...
...