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Music Predictions Using Deep Learning. Could LSTM Networks be the New Standard for Collaborative Filtering?
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2016 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Predicting the product a customer would like to buy is an increasingly important field of study and there are several different recommender system models that are used to make recommendations for users. Deep learning has shown effective results in a variety of predictive tasks but there haven’t been much research concerning its usage in recommender systems.

This thesis studies the effectiveness of using a long short term memory implementation (LSTM) of a recurrent neural network (RNN) as a recommender system by comparing it to one of the most common recommender system implementations, the matrix factorization method. A radio playlist dataset is used to train both the LSTM and the matrix factorization models with the intent of generating accurate predictions. We were unable to create a LSTM model with good performance and due to that we are unable to make any significant conclusions regarding whether or not LSTM networks outperform matrix factorization models.

Place, publisher, year, edition, pages
2016.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-187490OAI: oai:DiVA.org:kth-187490DiVA: diva2:930575
Supervisors
Examiners
Available from: 2016-05-24 Created: 2016-05-24 Last updated: 2016-05-24Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf