[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123423-en":3,"doc-seo-123423-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123423,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","RIADA - A Machine-Learning Based Infrastructure for Recognising the Emotions of Spotify Songs","Music emotions can improve personalization of services and content in music streaming. Prior machine-learning research tackles automatic music emotion recognition, yet results are typically limited to small repositories and often ignore the user’s emotional experience during listening, which reduces the ability to integrate emotions into online personalization mechanisms. RIADA presents an infrastructure of systems that annotate Spotify catalog songs based on users’ perception. It combines playlist mining and data services with emotion recognition model building using machine learning, music information retrieval, parallel architectures, and cloud computing to support Spotify-based applications.","International Journal of Interactive Multimedia and Artificial Intelligence, Vol. 8, Nº2  \nRIADA: A Machine-Learning Based Infrastructure for Recognising the Emotions of Spotify Songs  \nP. Álvarez, J. García de Quirós, S. Baldassarri *  \nComputer Science and Systems Engineering Department. María de Luna, 1, Ada Byron Building, Zaragoza, University of Zaragoza (Spain)  \nReceived 28 January 2021 | Accepted 21 February 2022 | Published 21 April 2022  \nAbstract   \nThe music emotions can help to improve the personalization of services and contents offered by music streaming providers. Many research works based on the use of machine learning techniques have addressed the problem of recognising the music emotions during the last years. Nevertheless, the results obtained are only applied on small-size music repositories and do not consider what the users feel when they listen to the songs. These issues prevent the existing proposals to be integrated into the personalization mechanisms ofthe online music providers. In this paper, we present the RIADA infrastructure which is composed by a set of systems able to annotate emotionally the catalog of songs offered by Spotify based on the users’ perception. RIADA works with the Spotify playlist miner and data services to build emotion recognition models that can solve the open challenges previously mentioned. Machine learning algorithms, music information retrieval techniques, architectures for parallelization of applications and cloud computing have been combined to develop a complex result of engineering able to integrate the music emotions into the Spotify-based applications.  \nI. Introduction  \nCurrently, the music streaming services are  \nof offering personalised media contents to  \nfacing the chal-lenge their users [1] . The  \nhuge size of their music catalogs has promoted the develop-ment of innovative tools that help users to find among so many choices the songs that best suit their tastes. Most of these tools analyse the users’profiles and listening habits applying artificial intelligence techniques (such as collaborative filtering or content-based filtering), and then make personalised music recommenda-tions to the users [2] . These automatic tools are compatible with other types of content access services, for example, with services that publish the playlists created by other users or with social networks in which the users can share their listening experience. In all these solutions there are some factors that play a relevant role in the process of selecting the music, such as the musical genre and the popularity of the songs, the listening context and the activity that the user is doing, or certain cultural criteria, for instance. Nevertheless, other interesting factors have not had too much prominence among the tools offered by the streaming ser-vices, for example, the music emotions.  \nThe relationship between music and emotions has been widely studied during the last years and the interest of including the users’emotions as a factor for the content personalization has promoted the research area commonly refereed to as Music Emotion Recognition (MER) [3]. The goal of this area is to annotate automatically the songs  \n* Corresponding author.  \nE-mail addresses: alvaper@unizar.es (P. Álvarez), jgarciaqg@unizar.es (J. García de Quirós), [sandra@unizar.es](sandra@unizar.es) (S. Baldassarri).  \nfrom an emotional point of view. These annotations usually represent the perceived or the felt emotions by the users when listening the songs, that is, the perception of emotions orthe induction of emotions [4]. These two emotional dimensions are clearly different: the former is related to the emotions expressed by the music through the songs’ structure and sound properties, whereas the second depends on the listener’s experience and is influenced by her/his mood and context, among other factors. During the last years machine learning and deep learning techniques are being widely used to det","cbCaie1r2rIUr451","https://ap.wps.com/l/cbCaie1r2rIUr451","pdf",1555712,1,14,"English","en",105,"# Abstract\n# Introduction\n## Music personalization and recommendation context\n## Music Emotion Recognition (MER) goals and emotion dimensions\n## Machine learning and deep learning approaches for MER","[{\"question\":\"What problem does RIADA address in music streaming personalization?\",\"answer\":\"RIADA targets the gap between emotion recognition research and real-world personalization, where existing approaches usually rely on small datasets and do not capture how users feel when listening to songs.\"},{\"question\":\"How does RIADA generate emotion annotations for Spotify songs?\",\"answer\":\"RIADA uses Spotify playlist miner and data services to build emotion recognition models that annotate the Spotify song catalog according to users’ perceived emotions.\"},{\"question\":\"What technical components are combined in RIADA?\",\"answer\":\"RIADA integrates machine learning algorithms, music information retrieval techniques, parallelization architectures for applications, and cloud computing to deliver an engineering solution suitable for Spotify-based applications.\"}]","RIADA - A Machine-Learning Based Infrastructure for Recognising the Emotions of Spotify Songs | PDF",1785816393,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"riada-a-machine-learning-based-infrastructure-for-recognising-the-emotions-of-spotify-songs-123423","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/riada-a-machine-learning-based-infrastructure-for-recognising-the-emotions-of-spotify-songs-123423/123423/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does RIADA address in music streaming personalization?","Question",{"text":76,"@type":77},"RIADA targets the gap between emotion recognition research and real-world personalization, where existing approaches usually rely on small datasets and do not capture how users feel when listening to songs.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does RIADA generate emotion annotations for Spotify songs?",{"text":81,"@type":77},"RIADA uses Spotify playlist miner and data services to build emotion recognition models that annotate the Spotify song catalog according to users’ perceived emotions.",{"name":83,"@type":74,"acceptedAnswer":84},"What technical components are combined in RIADA?",{"text":85,"@type":77},"RIADA integrates machine learning algorithms, music information retrieval techniques, parallelization architectures for applications, and cloud computing to deliver an engineering solution suitable for Spotify-based applications.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]