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

Abstract : Representing the information about music is a complex activity that involves different sub-tasks. This thesis manuscript mostly focuses on classical music, researching how to represent and exploit its information. The main goal is the investigation of strategies of knowledge representation and discovery applied to classical music, involving subjects such as Knowledge-Base population, metadata prediction, and recommender systems. We propose a complete workflow for the management of music metadata using Semantic Web technologies. We introduce a specialised 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 realised for testing the previous approaches and resources.
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Submitted on : Wednesday, October 6, 2021 - 6:33:10 PM
Last modification on : Tuesday, November 16, 2021 - 5:23:08 AM
Long-term archiving on: : Friday, January 7, 2022 - 7:49:28 PM


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  • HAL Id : tel-03368533, version 1


Pasquale Lisena. Knowledge-based music recommendation : models, algorithms and exploratory search. Information Retrieval [cs.IR]. Sorbonne Université, 2019. English. ⟨NNT : 2019SORUS614⟩. ⟨tel-03368533⟩



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