[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290477-105":59,"doc-detail-290477-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","developing-and-comparing-machine-learning-algorithms-for-music-recommendation","Developing and Comparing Machine Learning Algorithms for Music Recommendation","","Song skipping in music streaming reduces user satisfaction and harms subscription retention, especially when listeners repeatedly encounter tracks they actively dislike. This research investigates music recommendation approaches to better align delivered value with user expectations. Data collection uses the Spotify API, followed by exploratory visualization and text-based genre analysis, explicit and modality counts, and time-based popularity views. After preprocessing, four clustering algorithms (k-means, mini-batch k-means, Gaussian mixture, DBSCAN) are evaluated with the Calinski-Harabasz score, with k-means performing best. The selected model is then used to measure song similarity and generate recommendations.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/developing-and-comparing-machine-learning-algorithms-for-music-recommendation/290477/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/developing-and-comparing-machine-learning-algorithms-for-music-recommendation/290477.png","ImageObject",300,407,{"name":92,"@type":93},"Maeve","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",15,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why does song skipping matter for music streaming services?","Question",{"text":113,"@type":114},"Song skipping often makes users encounter tracks they dislike, which decreases user satisfaction and contributes to subscription cancellations.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How is the dataset collected in this study?",{"text":118,"@type":114},"The study collects data through the Spotify application programming interface (Spotify API).",{"name":120,"@type":111,"acceptedAnswer":121},"Which machine learning algorithm performed best for recommendation quality?",{"text":122,"@type":114},"K-means outperformed the other evaluated algorithms according to the Calinski-Harabasz score.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290477,1789641777,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},5909877438554,"https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272","INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION  \n[journal homepage :](journal homepage : www.joiv.org/index.php/joiv)[ www.joiv.org/index.php/joiv](journal homepage : www.joiv.org/index.php/joiv)  \nDeveloping and Comparing Machine Learning Algorithms for Music  \nRecommendation  \nYoon-Teck Baua,*, Puteri Ainna Ezzurin Mohd Rezaa, Kian-Chin Lee a  \nFaculty of Computing and Informatics, Multimedia University, Cyberjaya, Selangor, 63100, Malaysia [Corresponding author: ytbau@mmu.edu.my](Corresponding author: ytbau@mmu.edu.my)  \nAbstract—The increasing prevalence of song skipping in music streaming applications negatively impacts user satisfaction and subscription retention. Dissatisfaction often arises when users encounter songs they actively dislike, highlighting a gap between user expectations and the value offered by these services. To address this, music recommendation algorithms were researched and developed. Initially, data collection is started. Data collection is through the Spotify application programming interface. This initiation step sets the stage for subsequent exploratory data analysis. Exploratory data analysis examined the collected data to plot a bar chart for total songs released over the years, plot a bar chart for the popularity of songs based on the year it is released, visualize word cloud on frequently mentioned music genres, chart count plot for explicit songs, and chart count plot for song modalities. Data preprocessing involved cleaning the data, handling missing values, and checking for null values to prepare the application of machine learning algorithms. Four machine learning algorithms were applied, k-means, mini-batch k-means, Gaussian mixture, and density-based spatial clustering of applications with noise (DBSCAN), to analyze musical features like rhythm, tempo, and other relevant music attributes. The results showed that the k-means outperforms all other algorithms evaluated regarding recommendation quality, as measured by the Calinski-Harabasz score. Based on the evaluation, the best machine learning will then be applied to identify similarities between songs and be used to generate music recommendation results.  \nKeywords—Machine learning algorithms; music recommendation; Spotify API; K-means; mini batch k-means; gaussian mixture.  \nManuscript received 10 Apr. 2024; revised 28 Jun. 2024; accepted 11 Oct. 2024. Date of publication 30 Nov. 2024.  \nInternational Journal on Informatics Visualization is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.  \nI. INTRODUCTION  \nThe rapid expansion of music streaming has changed how we enjoy music, but keeping users happy and maintaining subscriptions are still significant challenges. A big factor is a song skipping, where users frequently encounter tracks they dislike, interrupting the smooth, personalized experience they expect. This frustration often leads to canceled subscriptions, as users feel the service needs to meet their needs. At the heart of the problem is the limitation of current music recommendation systems. While they provide suggestions, these systems often need to fully capture each listener's unique taste, resulting in a playlist filled with songs that do not hit the mark. This research proposes a more personalized approach to music recommendation. By aligning recommendations more closely with individual preferences and reducing exposure to disliked tracks, we aim to improve the listening experience, minimize frustration, and, ultimately, encourage long-term subscription loyalty in the music streaming world.  \nThe research question is: How will this research develop a music recommendation that leverages the best algorithm analyzing song similarities based on musical features such as energy, tempo, and listening history to generate personalized recommendations, minimize song skipping, and enhance user satisfaction within music streaming services? By effectively addressing this question, this research","cbCaiiZVoSEBrz5p","https://ap.wps.com/l/cbCaiiZVoSEBrz5p","pdf",4776151,11,"English","# Abstract\n# Introduction\n## Music Recommendation and Existing Algorithms","[{\"question\":\"Why does song skipping matter for music streaming services?\",\"answer\":\"Song skipping often makes users encounter tracks they dislike, which decreases user satisfaction and contributes to subscription cancellations.\"},{\"question\":\"How is the dataset collected in this study?\",\"answer\":\"The study collects data through the Spotify application programming interface (Spotify API).\"},{\"question\":\"Which machine learning algorithm performed best for recommendation quality?\",\"answer\":\"K-means outperformed the other evaluated algorithms according to the Calinski-Harabasz score.\"}]","Developing and Comparing Machine Learning Algorithms for Music Recommendation | PDF",28]