Graduate School and Research Center in Digital Sciences

Knowledge-based music recommendation: Models, algorithms and exploratory search

Lisena, Pasquale


Representing information about music is a complex activity that involves different sub-tasks. This thesis mostly focuses on classical music, researching how to represent and exploit rich metadata. Our main goal is to investigate knowledge representation and discovery strategies applied to classical music, including research topics such as Knowledge-Base population, metadata prediction and recommender systems. We first propose a complete workflow for the management of music metadata using Semantic Web technologies. We introduce a specialized ontology and a set of controlled vocabularies for the different concepts specific to music. Then, we present an approach for converting data, in order to go beyond the librarian practice currently in use, relying on mapping rules and interlinking with controlled vocabularies. Finally, we show how these data can be exploited. In particular, we study approaches based on embeddings computed on structured metadata, titles, and symbolic music for ranking and recommending music. Several demo applications have been realized for testing the previous approaches and resources.

Document Bibtex

Title:Knowledge-based music recommendation: Models, algorithms and exploratory search
Department:Data Science
Eurecom ref:6027
Copyright: © EURECOM. Personal use of this material is permitted. The definitive version of this paper was published in Thesis and is available at :
Bibtex: @phdthesis{EURECOM+6027, year = {2019}, title = {{K}nowledge-based music recommendation: {M}odels, algorithms and exploratory search}, author = {{L}isena, {P}asquale}, school = {{T}hesis}, month = {10}, url = {} }
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