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Recommendation of Korean-Pop Bands using Topic Modelling Algorithm and Myers-Briggs Type Indicator

Ibrahim, Nurul Hanis Binti (2023) Recommendation of Korean-Pop Bands using Topic Modelling Algorithm and Myers-Briggs Type Indicator. Masters thesis, Dublin, National College of Ireland.

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Enhancing 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.

Item Type: Thesis (Masters)
Mulwa, Catherine
Subjects: M Music and Books on Music > M Music
Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
B Philosophy. Psychology. Religion > Psychology > Cognitive psychology
Q Science > QA Mathematics > Algebra > Algorithms > Computer algorithms
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 18 May 2023 16:30
Last Modified: 18 May 2023 16:30

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