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Title:
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APPLICATION DEVELOPMENT FOR MUSIC
RECOMMENDATION SYSTEM USING DEEP
DETERMINISTIC POLICY GRADIENT |
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Author(s):
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Rathin S. Kamble, Sujala D. Shetty and Aljo Jose |
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ISBN:
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978-989-8704-34-4 |
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Editors:
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Pedro Isaías and Hans Weghorn |
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Year:
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2021 |
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Edition:
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Single |
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Type:
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Full |
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First Page:
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216 |
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Last Page:
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220 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Recommendation Systems works as an information filtering system which helps to feed and recommend content
personalized for the taste of the user. From the use in e-commerce to generic advertisement, recommender systems are
proven to be highly effective and go-to solution for personalized content promotion. This project aims to develop and
design a Machine Learning model which can be integrated into an Android application to help recommend music for the
app user. For this purpose, a Deep Deterministic Policy Gradient model was used along with an underlying architecture
for designing the android application which contains playlist of the user's songs and considering the likes and dislikes
from the user, the app with the help of the ML model helps suggest user an additional array of songs. |
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