Music Recommendation Based On User Contex, Using Python Frame Work : Ikechukwu Jude J
ABSTRACT
Tills work presents a new approach to recommend
suitable tracks from a collection ofsongs to the user. The goal ofthe system is
to recommend songs that are preferred by the user, are fresh to the user’s ear,
and fit the user’s listening pattern. “Forgetting Curve” is used to assess
freshness of a song and the user log is used to evaluate the preference. I
analyze user’s listening pattern to estimate the level of interest of the user
in the next song. Also, user behavior is treated on the song being played as
feedback to adjust the recommendation strategy for the next one. Furthermore,
this work proposes a method to classify songs in the hundreds Song Dataset
according to song genre. Since songs have several data types, several
sub-classifiers are trained by diCerent types of data. These sub-classifiers
are combined using both classifier authority and classification confidence for
a particular instance. In the experiments, the combined classifier surpasses
all of these sub-classifiers and the SVM classifier using concatenated vectors
from all data types. Finally, I develop an application to evaluate our approach
in the real world.
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-- (2025). Music Recommendation Based On User Contex, Using Python Frame Work : Ikechukwu Jude J. Repository.mouau.edu.ng: Retrieved Jun 12, 2025, from https://repository.mouau.edu.ng/work/view/music-recommendation-based-on-user-contex-using-python-frame-work-ikechukwu-jude-j-7-2
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--. "Music Recommendation Based On User Contex, Using Python Frame Work : Ikechukwu Jude J" Repository.mouau.edu.ng (2025). Accessed 12 Jun. 2025. https://repository.mouau.edu.ng/work/view/music-recommendation-based-on-user-contex-using-python-frame-work-ikechukwu-jude-j-7-2