A Session-Based Song Recommendation Approach Involving User Characterization along the Play Power-Law Distribution

[EN]In recent years, streaming music platforms have become very popular mainly due to the huge number of songs these systems make available to users. This enormous availability means that recommendation mechanisms that help users to select the music they like need to be incorporated. However, develo...

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Autores: Sánchez Moreno, Diego, López Batista, Vivian Félix, Muñoz Vicente, María Dolores, Gil González, Ana Belén, Moreno García, María Navelonga
Tipo de recurso: artículo
Estado:Versión borrador
Fecha de publicación:2020
País:España
Institución:Universidad de Salamanca (USAL)
Repositorio:GREDOS. Repositorio Institucional de la Universidad de Salamanca
OAI Identifier:oai:gredos.usal.es:10366/161569
Acceso en línea:http://hdl.handle.net/10366/161569
Access Level:acceso abierto
Palabra clave:1203 Ciencia de los ordenadores
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spelling A Session-Based Song Recommendation Approach Involving User Characterization along the Play Power-Law DistributionSánchez Moreno, DiegoLópez Batista, Vivian FélixMuñoz Vicente, María DoloresGil González, Ana BelénMoreno García, María Navelonga1203 Ciencia de los ordenadores[EN]In recent years, streaming music platforms have become very popular mainly due to the huge number of songs these systems make available to users. This enormous availability means that recommendation mechanisms that help users to select the music they like need to be incorporated. However, developing reliable recommender systems in the music field involves dealing with many problems, some of which are generic and widely studied in the literature while others are specific to this application domain and are therefore less well-known. This work is focused on two important issues that have not received much attention: managing gray-sheep users and obtaining implicit ratings. The first one is usually addressed by resorting to content information that is often difficult to obtain. The other drawback is related to the sparsity problem that arises when there are obstacles to gather explicit ratings. In this work, the referred shortcomings are addressed by means of a recommendation approach based on the users’ streaming sessions. The method is aimed at managing the well-known power-law probability distribution representing the listening behavior of users. This proposal improves the recommendation reliability of collaborative filtering methods while reducing the complexity of the procedures used so far to deal with the gray-sheep problemThis research has been supported by the Department of Education of the Junta de Castilla y León, Spain (ORDEN EDU/667/2019 - Grant ID: SA064G19)WILEY202520252020info:eu-repo/semantics/articleinfo:eu-repo/semantics/draftapplication/pdfhttp://hdl.handle.net/10366/161569reponame:GREDOS. Repositorio Institucional de la Universidad de Salamancainstname:Universidad de Salamanca (USAL)InglésSA064G19info:eu-repo/semantics/openAccessoai:gredos.usal.es:10366/1615692026-06-07T06:28:51Z
dc.title.none.fl_str_mv A Session-Based Song Recommendation Approach Involving User Characterization along the Play Power-Law Distribution
title A Session-Based Song Recommendation Approach Involving User Characterization along the Play Power-Law Distribution
spellingShingle A Session-Based Song Recommendation Approach Involving User Characterization along the Play Power-Law Distribution
Sánchez Moreno, Diego
1203 Ciencia de los ordenadores
title_short A Session-Based Song Recommendation Approach Involving User Characterization along the Play Power-Law Distribution
title_full A Session-Based Song Recommendation Approach Involving User Characterization along the Play Power-Law Distribution
title_fullStr A Session-Based Song Recommendation Approach Involving User Characterization along the Play Power-Law Distribution
title_full_unstemmed A Session-Based Song Recommendation Approach Involving User Characterization along the Play Power-Law Distribution
title_sort A Session-Based Song Recommendation Approach Involving User Characterization along the Play Power-Law Distribution
dc.creator.none.fl_str_mv Sánchez Moreno, Diego
López Batista, Vivian Félix
Muñoz Vicente, María Dolores
Gil González, Ana Belén
Moreno García, María Navelonga
author Sánchez Moreno, Diego
author_facet Sánchez Moreno, Diego
López Batista, Vivian Félix
Muñoz Vicente, María Dolores
Gil González, Ana Belén
Moreno García, María Navelonga
author_role author
author2 López Batista, Vivian Félix
Muñoz Vicente, María Dolores
Gil González, Ana Belén
Moreno García, María Navelonga
author2_role author
author
author
author
dc.subject.none.fl_str_mv 1203 Ciencia de los ordenadores
topic 1203 Ciencia de los ordenadores
description [EN]In recent years, streaming music platforms have become very popular mainly due to the huge number of songs these systems make available to users. This enormous availability means that recommendation mechanisms that help users to select the music they like need to be incorporated. However, developing reliable recommender systems in the music field involves dealing with many problems, some of which are generic and widely studied in the literature while others are specific to this application domain and are therefore less well-known. This work is focused on two important issues that have not received much attention: managing gray-sheep users and obtaining implicit ratings. The first one is usually addressed by resorting to content information that is often difficult to obtain. The other drawback is related to the sparsity problem that arises when there are obstacles to gather explicit ratings. In this work, the referred shortcomings are addressed by means of a recommendation approach based on the users’ streaming sessions. The method is aimed at managing the well-known power-law probability distribution representing the listening behavior of users. This proposal improves the recommendation reliability of collaborative filtering methods while reducing the complexity of the procedures used so far to deal with the gray-sheep problem
publishDate 2020
dc.date.none.fl_str_mv 2020
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/draft
format article
status_str draft
dc.identifier.none.fl_str_mv http://hdl.handle.net/10366/161569
url http://hdl.handle.net/10366/161569
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv SA064G19
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv WILEY
publisher.none.fl_str_mv WILEY
dc.source.none.fl_str_mv reponame:GREDOS. Repositorio Institucional de la Universidad de Salamanca
instname:Universidad de Salamanca (USAL)
instname_str Universidad de Salamanca (USAL)
reponame_str GREDOS. Repositorio Institucional de la Universidad de Salamanca
collection GREDOS. Repositorio Institucional de la Universidad de Salamanca
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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