[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119750-en":3,"doc-seo-119750-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},119750,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Adopting Machine Learning in Demographic Filtering for Movie Recommendation System","Big data has accelerated adoption of movie recommendation systems, yet common machine-learning challenges remain prominent: cold start and data sparsity. This study investigates a decision-making algorithm designed to improve early recommendations through precise parameters by applying a demographics filtering approach combined with k-means clustering. The method groups users into clusters based on gender, age group, and occupation, then selects representative user profiles using distances to cluster centers. Experiments with three clusters evaluate how demographic filtering influences preferred movie genres, supporting demographic filtering as an alternative direction for future technical development.","e-ISSN : 2716-621X  \nAdopting Machine Learning in Demographic Filtering for Movie Recommendation System  \nLee Jia Yin1, Noor Zuraidin Mohd Safar1*, Hazalila Kamaludin1, Noryusliza Abdullah1, Mohd Azahari Mohd Yusof1 , Catur Supriyanto2  \n1Fakulti Sains Komputer dan Teknologi Maklumat,  \nUniversiti Tun Hussein Onn Malaysia, Parit Raja, Batu Pahat, 86400, MALAYSIA  \n2Faculty of Computer Science,  \nUniversitas Dian Nuswantoro, Semarang, INDONESIA  \n*Corresponding Author  \nDOI: [https://doi.org/10.30880/jscdm.2023.04.01.001](https://doi.org/10.30880/jscdm.2023.04.01.001)  \nReceived 04 January 2023; Accepted 02 April 2023; Available online 25 May 2023  \nAbstract: In this era of big data explosion, humans widely use the movie recommendation system as an information tool. There are two common issues found in the machine learning movie recommendation system that is still undeniable: first, cold start, and second, data sparsity. To minimize the problems, a research study is conducted to find a decision-making algorithm to solve the complex start problem in a movie recommendation system with precise parameters. It involves the implementation of the proposed demographics filtering technique with the k-means clustering method. The research findings present the effects of demographic filtering for movie recommendations. Demographic filtering can group users into clusters based on gender, age group, and occupation. The clusters distribution representative group based on the top 100 results of the experiment. The user with the least distance to the cluster center is chosen as the usual group in that cluster. Three clusters were experimented: Cluster 0, Cluster 1, and Cluster 2. Cluster 0 has a representative group of male, college, or graduate students aged 25 to 34. Cluster 1 has a representative group of females, executive or managerial, aged 25 to 34. Cluster 2 has a representative group of males, sales or marketing aged 35 to 44. It is shown that user from different collection has various preferred movie genre. The preferred movie genre in Cluster 0 is action, adventure, comedy, drama, and war. Cluster 1 has preferred comedy, crime, drama, horror, romance, and sci-fi movie genres. Cluster 2 has chosen action, comedy, drama, filmnoir, mystery, and thriller movie genres. This research has contributed to the demographic filtering studies as an alternative solution for future technical development work.  \nKeywords: Machine learning, recommendation system, demographic filtering, K-means clustering  \n1. Introduction  \nThe advancement in technology is reaching new heights every day and it is obviously noticeable. To manage a large amount of data, machine learning that builds an analytical model automatically is introduced. People use machine learning to generate a recommender system that predicts most related recommendations using various computational statistics of datasets on the internet [1] . The recommender system is a kind of information filtering system that works to predict the preference or rating of an item. Generally, there are three recommendation system approaches. They are the content-based filtering, collaborative filtering, and hybrid filtering approach [2] .  \nInterestingly, famous movie streaming service application like Netflix uses CineMatch as their proprietary recommender system since 2000. CineMatch is software that is embedded in the Netflix website where it applies machine learning and data mining to analyze customers’ preferences on movie selection and recommend other movies that the customer might most likely enjoy [3] .  \nMeanwhile, the movie recommendation system is also used and introduced widely with the rise of machine learning and recommendation system in various areas. The content-based movie recommendation system works based on the similarity of movie types and attributes. While the collaborative filtering movie recommendation system works based on past interactions between users and movies on the certain plat","cbCair5tK8YGsu1b","https://ap.wps.com/l/cbCair5tK8YGsu1b","pdf",779416,1,12,"English","en",105,"# Introduction\n## Related Work\n### Recommendation System","[{\"question\":\"What two main problems does the movie recommendation system face according to the study?\",\"answer\":\"The study highlights cold start and data sparsity as the two unavoidable issues in machine-learning movie recommendation systems.\"},{\"question\":\"How does demographic filtering work in the proposed approach?\",\"answer\":\"Demographic filtering groups users into clusters using gender, age group, and occupation, then chooses representative users based on their distance to each cluster center.\"},{\"question\":\"What genres are associated with the representative groups of different clusters?\",\"answer\":\"Cluster 0 is linked to action, adventure, comedy, drama, and war; Cluster 1 to comedy, crime, drama, horror, romance, and sci-fi; and Cluster 2 to action, comedy, drama, filmnoir, mystery, and thriller.\"}]","Adopting Machine Learning in Demographic Filtering for Movie Recommendation System | PDF",1785726103,30,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"adopting-machine-learning-in-demographic-filtering-for-movie-recommendation-system","",{"@graph":36,"@context":85},[37,54,68],{"@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/adopting-machine-learning-in-demographic-filtering-for-movie-recommendation-system/119750/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What two main problems does the movie recommendation system face according to the study?","Question",{"text":75,"@type":76},"The study highlights cold start and data sparsity as the two unavoidable issues in machine-learning movie recommendation systems.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does demographic filtering work in the proposed approach?",{"text":80,"@type":76},"Demographic filtering groups users into clusters using gender, age group, and occupation, then chooses representative users based on their distance to each cluster center.",{"name":82,"@type":73,"acceptedAnswer":83},"What genres are associated with the representative groups of different clusters?",{"text":84,"@type":76},"Cluster 0 is linked to action, adventure, comedy, drama, and war; 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