{"id":2444,"date":"2019-10-23T12:42:59","date_gmt":"2019-10-23T12:42:59","guid":{"rendered":"http:\/\/skimai.com\/?p=2444"},"modified":"2024-05-20T07:38:38","modified_gmt":"2024-05-20T12:38:38","slug":"algoritmos-neuronales-de-recomendacion","status":"publish","type":"post","link":"https:\/\/skimai.com\/es\/neural-recommendation-algorithms\/","title":{"rendered":"\u00bfEstamos avanzando realmente en la recomendaci\u00f3n neuronal?"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_1 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">\u00cdndice<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Alternar tabla de contenidos\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewbox=\"0 0 24 24\" version=\"1.2\" baseprofile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1 ez-toc-heading-level-1'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/skimai.com\/es\/neural-recommendation-algorithms\/#Are_We_Really_Making_Progress_on_Neural_Recommendation_Approaches\" >\u00bfEstamos avanzando realmente en\nneuronales?<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/skimai.com\/es\/neural-recommendation-algorithms\/#A_summary_of_Maurizio_Ferrari_Dacrema_et_als_Recent_Article_at_RecSys_2019%E2%80%8B\" >Un resumen del reciente art\u00edculo de Maurizio Ferrari Dacrema, et al. en RecSys 2019<\/a><\/li><\/ul><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/skimai.com\/es\/neural-recommendation-algorithms\/#Neural_Recommendation_Algorithms\" >Algoritmos neuronales de recomendaci\u00f3n<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/skimai.com\/es\/neural-recommendation-algorithms\/#How_Progress_is_Measured\" >C\u00f3mo se mide el progreso<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/skimai.com\/es\/neural-recommendation-algorithms\/#Why_Are_These_Methods_Failing\" >\u00bfPor qu\u00e9 fallan estos m\u00e9todos?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/skimai.com\/es\/neural-recommendation-algorithms\/#Neural_Recommendation_Have_We_Improved\" >Recomendaci\u00f3n neuronal: \u00bfHemos mejorado?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/skimai.com\/es\/neural-recommendation-algorithms\/#Summary\" >Resumen<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h1><span class=\"ez-toc-section\" id=\"Are_We_Really_Making_Progress_on_Neural_Recommendation_Approaches\"><\/span>\u00bfEstamos avanzando realmente en<br \/>\n\u00bfEnfoques de recomendaci\u00f3n?<span class=\"ez-toc-section-end\"><\/span><\/h1>\n<pre><code>        <h4><span class=\"ez-toc-section\" id=\"A_summary_of_Maurizio_Ferrari_Dacrema_et_als_Recent_Article_at_RecSys_2019%E2%80%8B\"><\/span>Un resumen del reciente art\u00edculo de Maurizio Ferrari Dacrema, et al. en RecSys 2019<span class=\"ez-toc-section-end\"><\/span><\/h4>     \n    <h2><span class=\"ez-toc-section\" id=\"Neural_Recommendation_Algorithms\"><\/span><strong>Algoritmos neuronales de recomendaci\u00f3n<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>Los algoritmos de recomendaci\u00f3n se han hecho omnipresentes en todos los \u00e1mbitos comerciales, desde la p\u00e1gina de inicio \"yourstore\" de Amazon hasta las puntuaciones % de Netflix. En esencia, los algoritmos de recomendaci\u00f3n filtran grandes conjuntos de datos, como bases de datos de canciones o pel\u00edculas, utilizando diversos m\u00e9todos para encontrar los elementos m\u00e1s relevantes para un usuario. Para ello, el algoritmo examina el comportamiento anterior del usuario y utiliza los conocimientos adquiridos a partir de esas observaciones para recomendar los productos y medios que el usuario tiene m\u00e1s probabilidades de comprar, ver o escuchar. Se han hecho muchos intentos de aprovechar el aprendizaje autom\u00e1tico, especialmente las redes neuronales, para los sistemas de recomendaci\u00f3n. Dacrema et al. han escrito un esclarecedor art\u00edculo en el que se preguntan si realmente se est\u00e1 mejorando con respecto a las t\u00e9cnicas tradicionales. Seg\u00fan su art\u00edculo, \"...existen indicios... .de que los avances logrados -medidos en t\u00e9rminos de mejoras de precisi\u00f3n con respecto a los modelos existentes- no siempre son tan fuertes como se esperaba\". As\u00ed pues, si los avances no se captan con precisi\u00f3n, \u00bfc\u00f3mo miden actualmente los investigadores los avances, cu\u00e1les son los fallos de estos m\u00e9todos y si realmente hemos mejorado los algoritmos de recomendaci\u00f3n a\u00f1adiendo t\u00e9cnicas de aprendizaje autom\u00e1tico?<\/p><h2><span class=\"ez-toc-section\" id=\"How_Progress_is_Measured\"><\/span><strong>C\u00f3mo se mide el progreso<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2><p>El progreso en el rendimiento de los algoritmos se mide comparando el rendimiento de los nuevos algoritmos con el rendimiento de referencia de otros algoritmos de alcance. En concreto, las m\u00e9tricas m\u00e1s utilizadas son:<\/p><ul><li>Precisi\u00f3n: La capacidad de un modelo de clasificaci\u00f3n para identificar s\u00f3lo los puntos de datos relevantes.<\/li><li>Recuperaci\u00f3n: La capacidad de un modelo para encontrar todos los puntos de datos relevantes dentro de un conjunto de datos.<\/li><li>Ganancia acumulada descontada normalizada (NDCG): comparaci\u00f3n entre la lista de clasificaci\u00f3n de referencia (normalmente juzgada por humanos) y la lista de clasificaci\u00f3n del algoritmo.<\/li><\/ul><h2><span class=\"ez-toc-section\" id=\"Why_Are_These_Methods_Failing\"><\/span><strong>\u00bfPor qu\u00e9 fallan estos m\u00e9todos?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2><p>Aunque son varios los factores que contribuyen al fracaso de los actuales m\u00e9todos de evaluaci\u00f3n del progreso, Decrema et al. se\u00f1alan tres factores clave:<\/p><ol><li>Conjuntos de datos de referencia d\u00e9biles para la formaci\u00f3n y la evaluaci\u00f3n<\/li><li>M\u00e9todos d\u00e9biles utilizados para las nuevas l\u00edneas de base (utilizando algoritmos previamente publicados pero no verificados para la comparaci\u00f3n de rendimiento).<\/li><li>Incapacidad para comparar y <em>reproducir<\/em> resultados de los distintos documentos<\/li><\/ol><p>En particular, los autores se\u00f1alan la extrema falta de repetibilidad de los algoritmos publicados. Los autores se apresuran a se\u00f1alar que en el entorno de investigaci\u00f3n moderno, en el que el c\u00f3digo fuente y los conjuntos de datos est\u00e1n f\u00e1cilmente disponibles, los resultados publicados deber\u00edan ser triviales de recrear. Sin embargo, \"en realidad, hay... peque\u00f1os detalles relativos a la implementaci\u00f3n de los algoritmos y al procedimiento de evaluaci\u00f3n... que pueden influir en los resultados del experimento\". De hecho, los autores s\u00f3lo encontraron un total de siete art\u00edculos con c\u00f3digo fuente y conjuntos de datos susceptibles de reproducci\u00f3n de entre las docenas examinadas.<\/p><h2><span class=\"ez-toc-section\" id=\"Neural_Recommendation_Have_We_Improved\"><\/span><strong>Recomendaci\u00f3n neuronal: \u00bfHemos mejorado?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2><p>Dacrema et al. probaron en su art\u00edculo siete algoritmos publicados. Compararon los resultados de estos algoritmos, utilizando los datos empleados en los respectivos estudios, con los resultados de algoritmos tradicionales, mucho m\u00e1s sencillos. En su estudio, s\u00f3lo encontraron un algoritmo que superaba a los m\u00e9todos tradicionales: Variational Autoencoders for Collaborative Filtering (Mult-VAE), presentado por Liang et al. en 2018. Decrema et al. argumentan que Mult-VAE proporciona las siguientes mejoras de rendimiento:<\/p><ul><li>Los resultados de precisi\u00f3n obtenidos fueron entre 10% y 20% mejores que el m\u00e9todo lineal simple (SLIM) presentado por Xia Ning y George Karypis en 2011 en IDCM 11, que fue el mejor rendimiento del algoritmo de referencia.<\/li><li>Los resultados pudieron reproducirse con mejoras sobre SLIM de hasta 5% en todas las medidas de rendimiento.<\/li><li>Las mejoras del recuerdo de Mult-VAE sobre SLIM \"parecen s\u00f3lidas\".<\/li><\/ul><p>Decrema et al. concluyen afirmando \"As\u00ed, con Mult-VAE, encontramos un ejemplo en la literatura examinada en el que un m\u00e9todo m\u00e1s complejo era mejor... que cualquiera de nuestras t\u00e9cnicas de referencia en todas las configuraciones\".<\/p><h2><span class=\"ez-toc-section\" id=\"Summary\"><\/span><strong>Resumen<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2><p>Por muy tentador que sea declarar el \u00e9xito y publicar algoritmos y resultados novedosos, el equipo de Dacrema ha demostrado que en realidad no estamos mejorando, o al menos no mucho. Su art\u00edculo concluye afirmando: \"Nuestro an\u00e1lisis indica que... la mayor\u00eda de los trabajos revisados pueden ser superados, al menos en algunos conjuntos de datos, por algoritmos conceptual y computacionalmente m\u00e1s sencillos\". Por lo tanto, por muy tentador que sea aplicar el aprendizaje autom\u00e1tico a todas las aplicaciones de an\u00e1lisis de datos, los sistemas de recomendaci\u00f3n han demostrado hasta ahora ser una aplicaci\u00f3n para la que el aprendizaje autom\u00e1tico no ha mejorado el rendimiento de los algoritmos; al menos, no todav\u00eda.<\/code><\/pre>","protected":false},"excerpt":{"rendered":"<p>Are We Really Making Progress on Neural Recommendation Approaches? A summary of Maurizio Ferrari Dacrema, et al.\u2019s Recent Article at RecSys 2019\u200b Neural Recommendation AlgorithmsRecommendation algorithms have become ubiquitous across commercial fields, from the Amazon \u201cyourstore\u201d splash page to Netflix\u2019s matching % scores. Recommendation algorithms in essence filter large sets of data, i.e. song or [&hellip;]<\/p>\n","protected":false},"author":1003,"featured_media":2457,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"single-custom-post-template.php","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[67],"tags":[],"class_list":["post-2444","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ml-nlp"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v24.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Is There Progress on Neural Recommendation? - Skim AI<\/title>\n<meta name=\"description\" content=\"Machine learning has widespread impact but are we really making progress with neural recommendation algorithms with machine learning?\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/skimai.com\/es\/algoritmos-neuronales-de-recomendacion\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Is There Progress on Neural Recommendation? 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