{"id":91702,"date":"2018-03-11T10:10:31","date_gmt":"2018-03-11T10:10:31","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/music-recommendation-and-discovery-in-the-long-tail\/"},"modified":"2018-03-11T10:10:31","modified_gmt":"2018-03-11T10:10:31","slug":"music-recommendation-and-discovery-in-the-long-tail","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/musica-y-musicologia\/music-recommendation-and-discovery-in-the-long-tail\/","title":{"rendered":"Music recommendation and discovery in the long tail"},"content":{"rendered":"<h2>Tesis doctoral de <strong> Oscar Celma Herrada <\/strong><\/h2>\n<p>Actualmente, el consumo de m\u00fasica est\u00e1 sesgada hacia algunos artistas muy populares.  Por ejemplo, en el a\u00f1o 2007 s\u00f3lo el 1% de todas las canciones en formato digital representaron el 80% de las ventas.  de igual modo, \u00fanicamente 1.000 \u00e1lbumes representaron el 50% de todas las ventas, y el 80% de todos los \u00e1lbumes vendidos se compraron menos de 100 veces. Existe, pues, una necesidad de ayudar a los usuarios a filtrar, descubrir, personalizar y recomendar m\u00fasica a partir de la enorme cantidad de contenido musical existente.  los algoritmos de recomendaci\u00f3n musical existentes intentan predecir con precisi\u00f3n lo que la gente quiere escuchar. Sin embargo, muy a menudo estos algoritmos tienden a recomendar o bien artistas famosos, o bien artistas ya conocidos de antemano por el usuario. Esto disminuye la eficacia y la utilidad de las recomendaciones, ya que estos algoritmos se centran en mejorar la precisi\u00f3n de las recomendaciones. Con lo cu\u00e1l, tratan de predecir lo que un usuario pudiera escuchar o comprar, independientemente de lo \u00fatiles que sean las recomendaciones generadas.  en este sentido, la tesis destaca la importancia de que el usuario valore las recomendaciones propuestas. Por ello, modelamos la curva de popularidad de los artistas con el fin de recomendar m\u00fasica interesante y, a la vez, desconocida para el usuario. Las principales contribuciones de esta tesis son: (i) un nuevo enfoque basado en el an\u00e1lisis de redes complejas y la popularidad de los productos, aplicada a los sistemas de recomendaci\u00f3n, (ii) una evaluaci\u00f3n centrada en el usuario que mide la calidad y la novedad de las recomendaciones, y (iii) dos prototipos que implementan las ideas derivadas de la labor te\u00f3rica.   los resultados obtenidos tienen importantes implicaciones para los sistemas de recomendaci\u00f3n que ayudan al usuario a explorar y descubrir contenidos que le puedan gustar.<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>Music recommendation and discovery in the long tail<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 Music recommendation and discovery in the long tail <\/li>\n<li><strong>Autor:<\/strong>\u00a0 Oscar Celma Herrada <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Pompeu fabra<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 16\/02\/2009<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Direcci\u00f3n y tribunal<\/h3>\n<ul>\n<li><strong>Director de la tesis<\/strong>\n<ul>\n<li>Xavier Serra Casals<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: ricardo Baeza yates <\/li>\n<li>josep lLuis Arcos rosell (vocal)<\/li>\n<li>stephan Baumann (vocal)<\/li>\n<li>marc Torrens arnal (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Oscar Celma Herrada Actualmente, el consumo de m\u00fasica est\u00e1 sesgada hacia algunos artistas muy populares. Por ejemplo, [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"footnotes":""},"categories":[16880,12646,9132,18712],"tags":[60350,189900,189899,16549,128668,38388],"class_list":["post-91702","post","type-post","status-publish","format-standard","hentry","category-construccion-de-algoritmos","category-funciones-de-varias-variables-complejas","category-musica-y-musicologia","category-pompeu-fabra","tag-josep-lluis-arcos-rosell","tag-marc-torrens-arnal","tag-oscar-celma-herrada","tag-ricardo-baeza-yates","tag-stephan-baumann","tag-xavier-serra-casals"],"_links":{"self":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts\/91702","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/comments?post=91702"}],"version-history":[{"count":0,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts\/91702\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/media?parent=91702"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/categories?post=91702"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/tags?post=91702"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}