[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118552-en":3,"doc-seo-118552-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},118552,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Spotify Song Analysis by Statistical Machine Learning","This paper studies Artificial Intelligence within the music streaming context, focusing on redesigning the Spotify playlist creation workflow using statistical machine learning algorithms. Spotify is selected for its leading role and its high personalization, driven by large-scale data about users and listening habits that enable targeted recommendations. The study also addresses AI’s use in mood-oriented playlist identification from a large sample of Spotify songs.","International Journal of Music Science, Technology and Art  \n(IJMSTA) 5 (1): 39-51, Music Academy “Studio Musica”, 2023 ISSN: 2612-2146 (Online)  \nSpotify Song Analysis by Statistical Machine Learning  \nFederica Biazzo1* and Matteo Farné2*  \n1* [Corresponding author: Federica.171@](Corresponding author: Federica.171@hotmail.it)[hotmail.it](Corresponding author: Federica.171@hotmail.it)[ ](Corresponding author: Federica.171@hotmail.it)2* [Corresponding author: matteo.farne@unibo.it](Corresponding author: matteo.farne@unibo.it)  \nARTICLE INFO  \nReceived: May 30, 2023  \nAccepted: June 16, 2023  \nPublished: June 30, 2023  \nDOI: [https://doi.org/10.48293/IJMSTA-97](https://doi.org/10.48293/IJMSTA-97)  \nKeywords:  \nArtificial Intelligence Spotify  \nMachine Learning Mood Playlists Genre Playlists  \nABSTRACT  \nThis paper aims to study the use of Artificial Intelligence in the music streaming scenario. Specifically, the Spotify playlist creation process is redesigned by employing statistical machine learning algorithms. Spotify is chosen because it is the undisputed leader in music streaming, founding its success on the listening experience offered to its users. The level of personalization of the Spotify service is high and is made possible precisely because of Artificial Intelligence: by collecting masses of data about users and their listening habits, Spotify is able to understand the single user’s interests and make targeted recommendations. Artificial Intelligence is also being used for another aspect: the creation of playlists, which collect different songs in order to satisfy every need or desire of the user. This work aims precisely at identifying the most relevant playlists by \"Mood” through machine learning algorithms from a large sample of Spotify songs.  \nCopyright © 2023 Author et al., licensed to IJMSTA. This is an open access article distributed under the terms of the Creative Commons Attribution licence ([http://creativecommons.org/licenses/by/3.0/](http://creativecommons.org/licenses/by/3.0/)), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.  \n1 Introduction  \nEvery day with our simple daily actions we generate huge amounts of data, something like millions of gigabytes per day. This massive amount of data is used by companies to obtain information about our behavior that can be useful for their business: for example, advertisements on websites that are based on our search history in order to offer products or services that we like; streaming apps possessing a recommendation system that uses the history of watched films or listened songs in order to collect information about what is of most interest to the customer and to offer suggestions; online shopping apps that recommend products based on the history, interests and preferences of the buyer.  \nAll this is possible thanks to one of the most interesting new fields of research in science and engineering: Artificial Intelligence. It is defined as a system capable of learning, more precisely a set of instructions (algorithms) that allows computers to learn from experience (collected data) and, via this knowledge, to solve new problems in an ever-changing environment. A form of classification of AI which is based on the degree of intelligence allowed by the system has been identified: weak AI and strong AI [1] .  \nThe weak AI hypothesis is based on the idea that machines can act as if they were intelligent. A weak AI is a system that acts like the human mind: it collects data, studies them, works out solutions and chooses the most efficient one for the goal. In doing so, the machine “learns” to reduce the output error. Systems that are used to complete a specific task and require the presence of a supervisor to operate fall into this category.  \nThe strong AI hypothesis, on the other hand, is based on the claim that machines that act intelligently actually think and do not merely simulate thought. Sy","cbCaiqpCMUjXGmgr","https://ap.wps.com/l/cbCaiqpCMUjXGmgr","pdf",921884,2,1,13,"English","en",105,"# Introduction\n## Artificial Intelligence and data-driven learning\n## Weak AI vs strong AI\n## Superintelligent AI\n## Spotify as an AI-based streaming platform\n## Spotify personalization and playlist creation","[{\"question\":\"What is the main goal of the study about Spotify playlists?\",\"answer\":\"The study aims to identify the most relevant mood playlists using machine learning algorithms trained on a large sample of Spotify songs.\"},{\"question\":\"Why is Spotify chosen as the research focus?\",\"answer\":\"Spotify is presented as the undisputed leader in music streaming, with success attributed to a highly personalized listening experience supported by AI-driven recommendations.\"},{\"question\":\"How does the personalization process described in the paper begin?\",\"answer\":\"Personalization starts from user registration, where users self-identify demographic details, 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