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Music recommendations with deep learning

Rynegardh, Carl LU (2019) EITM01 20172
Department of Electrical and Information Technology
Abstract
In this thesis we apply deep reinforcement learning to the problem of recom-
mending music. A content-based approach is taken, and features from music
is extracted with a pretrained deep learning music-tagger. For training, user-
interactions are simulated.
Popular Abstract
In this thesis multiple machine learning, and specifically deep learning techniques were used to recommend music to users interacting with a music recommendation system. To recommend music a user enjoys as soon as possible deep reinforcement learning was applied, and because no real users were available we trained the system with simulated users. For reducing the amount of information in music, features were extracted from a pre-trained music-tagger.
Please use this url to cite or link to this publication:
author
Rynegardh, Carl LU
supervisor
organization
course
EITM01 20172
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Reinforcement learning, deep learning, music information retrieval
report number
LU/LTH-EIT 2019-718
language
English
id
8993717
date added to LUP
2019-09-10 09:35:34
date last changed
2019-09-10 09:35:34
@misc{8993717,
  abstract     = {{In this thesis we apply deep reinforcement learning to the problem of recom-
mending music. A content-based approach is taken, and features from music
is extracted with a pretrained deep learning music-tagger. For training, user-
interactions are simulated.}},
  author       = {{Rynegardh, Carl}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Music recommendations with deep learning}},
  year         = {{2019}},
}