Neural Approaches to Decode Semantic Similarities in Spanish Song Lyrics for Enhanced Recommendation Systems

Ghajari Espinosa, Adrián. (2024). Neural Approaches to Decode Semantic Similarities in Spanish Song Lyrics for Enhanced Recommendation Systems Master Thesis, Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Lenguajes y Sistemas Informáticos

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Título Neural Approaches to Decode Semantic Similarities in Spanish Song Lyrics for Enhanced Recommendation Systems
Autor(es) Ghajari Espinosa, Adrián
Abstract This dissertation explores the enhancement of music recommendation systems by integrating semantic similarity in Spanish song lyrics, utilizing advancements in machine learning and natural language processing (NLP), including both supervised and unsupervised approaches. It addresses the gap in current recommendation practices, which often overlook the rich semantic content of lyrics, despite their potential to significantly personalize music recommendations. Through theoretical insights into word embeddings and transfer learning, the development of the LyricSIM dataset for assessing lyric similarity, and empirical evaluations of models designed to distinguish between similar and non-similar song pairs, this research proposes a novel, lyrics-driven approach to music recommendation. Focused on the Spanish-speaking market, where Latin music is prevalent, this study contributes to the field by demonstrating how NLP technologies can refine music recommendations, addressing challenges like the cold start problem and enhancing the diversity of music recommendations, thereby offering a more personalized and engaging user experience in the streaming era.
Notas adicionales Trabajo Final de Máster Universitario en Tecnologías del lenguaje. UNED
Materia(s) Ingeniería Informática
Editor(es) Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Lenguajes y Sistemas Informáticos
Director/Tutor Benito-Santos, Alejandro
Fresno Fernández, Víctor Diego
Fecha 2024-02
Formato application/pdf
Identificador bibliuned:master-ETSInformatica-TL-Aghajari
http://e-spacio.uned.es/fez/view/bibliuned:master-ETSInformatica-TL-Aghajari
Idioma eng
Versión de la publicación acceptedVersion
Nivel de acceso y licencia http://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
Tipo de recurso master Thesis
Tipo de acceso Acceso abierto

 
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Creado: Fri, 15 Mar 2024, 19:06:03 CET