{"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-de-recomendacao-neural","status":"publish","type":"post","link":"https:\/\/skimai.com\/pt\/neural-recommendation-algorithms\/","title":{"rendered":"Estamos realmente a fazer progressos nas abordagens de recomenda\u00e7\u00e3o neural?"},"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 o \u00edndice\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Alternar<\/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\/pt\/neural-recommendation-algorithms\/#Are_We_Really_Making_Progress_on_Neural_Recommendation_Approaches\" >Estamos realmente a fazer progressos nas abordagens de\nRecomenda\u00e7\u00f5es Neurais?<\/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\/pt\/neural-recommendation-algorithms\/#A_summary_of_Maurizio_Ferrari_Dacrema_et_als_Recent_Article_at_RecSys_2019%E2%80%8B\" >Um resumo do artigo recente de Maurizio Ferrari Dacrema, et al. na 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\/pt\/neural-recommendation-algorithms\/#Neural_Recommendation_Algorithms\" >Algoritmos de recomenda\u00e7\u00e3o neural<\/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\/pt\/neural-recommendation-algorithms\/#How_Progress_is_Measured\" >Como s\u00e3o medidos os progressos<\/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\/pt\/neural-recommendation-algorithms\/#Why_Are_These_Methods_Failing\" >Porque \u00e9 que estes m\u00e9todos est\u00e3o a falhar?<\/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\/pt\/neural-recommendation-algorithms\/#Neural_Recommendation_Have_We_Improved\" >Recomenda\u00e7\u00e3o Neural: Melhor\u00e1mos?<\/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\/pt\/neural-recommendation-algorithms\/#Summary\" >Resumo<\/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>Estamos realmente a fazer progressos no dom\u00ednio neural?<br \/>\nRecomenda\u00e7\u00f5es de abordagens?<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>Um resumo do artigo recente de Maurizio Ferrari Dacrema, et al. na RecSys 2019<span class=\"ez-toc-section-end\"><\/span><\/h4>     \n    <h2><span class=\"ez-toc-section\" id=\"Neural_Recommendation_Algorithms\"><\/span><strong>Algoritmos de recomenda\u00e7\u00e3o neural<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>Os algoritmos de recomenda\u00e7\u00e3o tornaram-se omnipresentes em todos os dom\u00ednios comerciais, desde a p\u00e1gina inicial \"yourstore\" da Amazon at\u00e9 \u00e0s pontua\u00e7\u00f5es % correspondentes da Netflix. Os algoritmos de recomenda\u00e7\u00e3o filtram essencialmente grandes conjuntos de dados, ou seja, bases de dados de can\u00e7\u00f5es ou filmes, utilizando uma variedade de m\u00e9todos para descobrir os itens mais relevantes para um utilizador. O algoritmo f\u00e1-lo analisando o comportamento passado de um utilizador e utilizando os conhecimentos adquiridos a partir dessas observa\u00e7\u00f5es para recomendar produtos e meios de comunica\u00e7\u00e3o que o utilizador tem maior probabilidade de comprar, ver ou ouvir. Foram feitas muitas tentativas para utilizar a aprendizagem autom\u00e1tica, especialmente as redes neuronais, nos sistemas de recomenda\u00e7\u00e3o. Embora exista uma grande quantidade de investiga\u00e7\u00e3o que alega melhorias nas recomenda\u00e7\u00f5es de v\u00e1rios algoritmos, Dacrema et al. escreveram um artigo esclarecedor que pergunta: estamos realmente a melhorar em rela\u00e7\u00e3o \u00e0s t\u00e9cnicas tradicionais? De acordo com o artigo, \"... h\u00e1 ind\u00edcios ... de que o progresso alcan\u00e7ado - medido em termos de melhorias de precis\u00e3o em rela\u00e7\u00e3o aos modelos existentes - nem sempre \u00e9 t\u00e3o forte como se esperava\". Assim, se o progresso n\u00e3o est\u00e1 a ser captado com precis\u00e3o, como \u00e9 que os investigadores est\u00e3o atualmente a medir o progresso, quais s\u00e3o as falhas destes m\u00e9todos e ser\u00e1 que melhor\u00e1mos realmente os algoritmos de recomenda\u00e7\u00e3o ao adicionar t\u00e9cnicas de aprendizagem autom\u00e1tica?<\/p><h2><span class=\"ez-toc-section\" id=\"How_Progress_is_Measured\"><\/span><strong>Como s\u00e3o medidos os progressos<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2><p>O progresso no desempenho do algoritmo \u00e9 medido comparando o desempenho do novo algoritmo com o desempenho de base de outros algoritmos de extens\u00e3o. Em particular, as m\u00e9tricas mais utilizadas s\u00e3o:<\/p><ul><li>Precis\u00e3o: A capacidade de um modelo de classifica\u00e7\u00e3o para identificar apenas os pontos de dados relevantes.<\/li><li>Recupera\u00e7\u00e3o: A capacidade de um modelo para encontrar todos os pontos de dados relevantes num conjunto de dados.<\/li><li>Ganho cumulativo descontado normalizado (NDCG): a compara\u00e7\u00e3o entre a lista classificada de base (normalmente avaliada por humanos) e a lista classificada do algoritmo.<\/li><\/ul><h2><span class=\"ez-toc-section\" id=\"Why_Are_These_Methods_Failing\"><\/span><strong>Porque \u00e9 que estes m\u00e9todos est\u00e3o a falhar?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2><p>Embora v\u00e1rios factores contribuam para o fracasso dos actuais m\u00e9todos de avalia\u00e7\u00e3o dos progressos, Decrema et al. apontam tr\u00eas factores-chave:<\/p><ol><li>Conjuntos de dados de base fracos para forma\u00e7\u00e3o e avalia\u00e7\u00e3o<\/li><li>M\u00e9todos fracos utilizados para novas linhas de base (utilizando algoritmos previamente publicados mas n\u00e3o verificados para compara\u00e7\u00e3o do desempenho)<\/li><li>Incapacidade de comparar e <em>reproduzir<\/em> resultados entre documentos<\/li><\/ol><p>Em particular, os autores chamam a aten\u00e7\u00e3o para a extrema falta de repetibilidade dos algoritmos publicados. Os autores s\u00e3o r\u00e1pidos a salientar que, no ambiente de investiga\u00e7\u00e3o moderno, em que o c\u00f3digo-fonte e os conjuntos de dados s\u00e3o disponibilizados prontamente, os resultados publicados deveriam ser triviais para recriar. No entanto, \"na realidade, existem ... pequenos pormenores relativos \u00e0 implementa\u00e7\u00e3o dos algoritmos e ao procedimento de avalia\u00e7\u00e3o ... que podem ter um impacto nos resultados da experi\u00eancia\". De facto, os autores s\u00f3 encontraram um total de sete artigos com c\u00f3digo-fonte e conjuntos de dados pass\u00edveis de reprodu\u00e7\u00e3o entre as dezenas examinadas.<\/p><h2><span class=\"ez-toc-section\" id=\"Neural_Recommendation_Have_We_Improved\"><\/span><strong>Recomenda\u00e7\u00e3o Neural: Melhor\u00e1mos?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2><p>Dacrema et al. testaram sete algoritmos publicados no seu artigo. Compararam os resultados destes algoritmos, utilizando os dados usados nos respectivos estudos, com os resultados dos algoritmos tradicionais, muito mais simples. No seu estudo, encontraram apenas um algoritmo que superou os m\u00e9todos tradicionais: Variational Autoencoders for Collaborative Filtering (Mult-VAE), apresentado por Liang et al. em 2018. Decrema et al. argumentam que o Mult-VAE proporciona as seguintes melhorias de desempenho:<\/p><ul><li>Os resultados de precis\u00e3o obtidos foram entre 10% e 20% melhores do que o m\u00e9todo linear simples (SLIM) apresentado por Xia Ning e George Karypis em 2011 no IDCM 11, que foi o melhor desempenho do algoritmo de base.<\/li><li>Os resultados podem ser reproduzidos com melhorias em rela\u00e7\u00e3o ao SLIM de at\u00e9 5% em todas as medidas de desempenho.<\/li><li>As melhorias na recorda\u00e7\u00e3o do Mult-VAE em rela\u00e7\u00e3o ao SLIM \"parecem s\u00f3lidas\".<\/li><\/ul><p>Decrema et al. concluem afirmando: \"Assim, com Mult-VAE, encontr\u00e1mos um exemplo na literatura examinada em que um m\u00e9todo mais complexo foi melhor ... do que qualquer uma das nossas t\u00e9cnicas de base em todas as configura\u00e7\u00f5es.\"<\/p><h2><span class=\"ez-toc-section\" id=\"Summary\"><\/span><strong>Resumo<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2><p>Por muito tentador que seja declarar o sucesso e publicar novos algoritmos e resultados, a equipa de Dacrema mostrou que n\u00e3o estamos a melhorar, ou pelo menos n\u00e3o muito. O artigo conclui afirmando: \"A nossa an\u00e1lise indica que ... a maioria dos trabalhos analisados pode ser ultrapassada, pelo menos em alguns conjuntos de dados, por algoritmos concetualmente e computacionalmente mais simples.\" Por conseguinte, por muito tentador que seja aplicar a aprendizagem autom\u00e1tica a todas as aplica\u00e7\u00f5es de an\u00e1lise de dados, os sistemas de recomenda\u00e7\u00e3o provaram at\u00e9 agora ser uma aplica\u00e7\u00e3o em que a aprendizagem autom\u00e1tica n\u00e3o melhorou o desempenho dos algoritmos; pelo menos, ainda n\u00e3o.<\/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\/pt\/algoritmos-de-recomendacao-neural\/\" \/>\n<meta property=\"og:locale\" content=\"pt_PT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Is There Progress on Neural Recommendation? 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