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An Intelligent User-specific Music Recommendation Engine - using Machine Learning

Mungekar, Akshay (2019) An Intelligent User-specific Music Recommendation Engine - using Machine Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

The world has turned out to be more of digital as per the recent statistics. A huge amount of digital data generated from various multimedia sources is consumed
and processed daily. In the online music streaming applications, users expect the music to be recommended as per their interests which is not the case with most of
the current music applications. Thus, an intellectual music recommendation system (RS) focused towards generating user-specific music based on their usage pattern has been proposed. This system takes the users behaviour logs into consideration to recommend the music of their interest. Also, most importantly, it only keeps the legitimate music tracks to make the RS more trustworthy. Thus, a number of unique Machine Learning (ML) based approaches have been implemented to develop an effective RS. The aim of this research is to implement a unique and highly efficient user-based music recommendation system using various ML algorithms chosen from previous studies. The methods used to implement the proposed RS is based on the effective results obtained from the previous researches. Thus, integrating such approaches with the existing ML models like Gradient-boosting decision trees, SVM, Nave Bayes and Random Forest are expected to obtain highly accurate performance in terms of recommendations.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software

H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Music Industry
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: CAOIMHE NI MHAICIN
Date Deposited: 06 Dec 2019 11:38
Last Modified: 06 Dec 2019 11:38
URI: http://norma.ncirl.ie/id/eprint/4110

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