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Experiments with a content-based recommendation approach show that the combined methods produce a range of K-pop bands aligned with at least two personality types defined in the framework.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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Indicator  \nMSc Research Project  \nData Analytics  \nNurul Hanis Binti Ibrahim  \nStudent ID: x20246862  \nSchool of Computing National College of Ireland  \nSupervisor: Dr. Catherine Mulwa  \nNational College of Ireland Project Submission Sheet School of Computing  \n| Student Name: | Nurul Hanis Binti Ibrahim |\n| --- | --- |\n| Student ID: | x20246862 |\n| Programme: | Data Analytics |\n| Year: | 2022 |\n| Module: | MSc Research Project |\n| Supervisor: | Dr. Catherine Mulwa |\n| Submission Due Date: | 15/12/2022 |\n| Project Title: | Recommendation of Korean-Pop Bands using Topic Modelling Algorithm and Myers-Briggs Type Indicator |\n| Word Count: | 3381 |\n| Page Count: | 17 |\n\nI hereby certify that the information contained in this (my submission) is information pertaining to research I conducted for this project. All information other than my own contribution will be fully referenced and listed in the relevant bibliography section at the rear of the project.  \nALL internet material must be referenced in the bibliography section. Students are required to use the Referencing Standard specified in the report template. To use other author’s written or electronic work is illegal (plagiarism) and may result in disciplinary action.  \n\n| Signature: | Nurul Hanis Binti Ibrahim |\n| --- | --- |\n| Date: | 30th January 2023 |\n\nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST:  \n\n| Attach a completed copy of this sheet to each project (including multiple copies) . | □ |\n| --- | --- |\n| Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies) . | □ |\n| You must ensure that you retain a HARD COPY of the project, both foryour own reference and in case a project is lost or mislaid. It is not sufficient to keep a copy on computer. | □ |\n\nAssignments that are submitted to the Programme Coordinator office must be placed into the assignment box located outside the office.  \n\n| Office Use Only |  |\n| --- | --- |\n| Signature: |  |\n| Date: |  |\n| Penalty Applied (if applicable): |  |\n\nRecommendation of Korean-Pop Bands using Topic Modelling Algorithm and Myers-Briggs Type  \nIndicator  \nNurul Hanis Binti Ibrahim  \nx20246862  \nAbstract  \nEnhancing the quality of information retrieval tasks comes a greater challenge for music streaming services. Particularly, in researching and developing a robust recommendation algorithm that fulfils the demand of users in providing music artist recommendations without being biased towards particular personality types as much as possible. In the light of this, this research focus on recommending the novel Korean-pop bands by utilising the Latent Dirichlet Allocation, a topic modelling algorithm and a personality framework, the Myers-Briggs Type Indicator. Practical experiments of the content-based recommendation algorithm shows that the combination of the aforementioned has successfully produce a variety of K-pop bands in accordance to two of the personality type listed in the framework.  \n1 Introduction  \n1.1 Background and Motivation  \nKorean-pop (K-pop) songs in digital format have reach billions of its listeners across the globe with the rapid advancement of technologies. This phenomenon leads to the soaring demand for unique K-pop band recommendations to its avid and active listeners respectively. Adjacently, pressuring music streaming services, such as Spotify, Apple Music and YouTube Music, in catering to the listeners’ distinctive personalities. Generally, a domain-centric recommender engine, such as the K-pop bands in this scope of research, operates when the algorithms produce a match between the users’ representation and the band’s representation to find the relevant band to the users. Currently in practice, the recommender algorithms and the parameters adopted by the music streaming services are lacking in providing the precise band recommendations for diverse personal","cbCainK3N84Hu19S","https://ap.wps.com/l/cbCainK3N84Hu19S","pdf",1931604,"English","# Abstract\n# 1 Introduction\n## 1.1 Background and Motivation\n## 1.2 Research Question, Objectives and Document Roadmap","[{\"question\":\"What problem does the research address for music streaming recommendations?\",\"answer\":\"The research targets the difficulty of producing precise K-pop band recommendations for diverse user personalities without strong bias toward particular personality types.\"},{\"question\":\"Which algorithms and frameworks are combined in the study?\",\"answer\":\"The study combines Latent Dirichlet Allocation (LDA) for topic modelling with the Myers-Briggs Type Indicator (MBTI) personality framework inside a content-based recommender approach.\"},{\"question\":\"How is the recommendation evaluated according to the abstract?\",\"answer\":\"Practical experiments of the content-based recommendation algorithm show that the combination successfully generates K-pop bands corresponding to at least two MBTI personality types listed in the framework.\"}]","Recommendation of Korean-Pop Bands using Topic Modelling Algorithm and Myers-Briggs Type Indicator | PDF",48]