Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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
RNN-based sequence prediction as an alternative or complement to traditional recommender systems
KTH, School of Computer Science and Communication (CSC).
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
RNN-baserad sekvensförutsägelse som ett alternativ eller kimplement till traditionella recommender-system (Swedish)
Abstract [en]

The recurrent neural networks have the ability to grasp the temporal patterns withinthe data. This is a property that can be used in order to help a recommender system bettertaking into account the user past history. Still the dimensionality problem that raiseswithin the recommender system field also raises here as the number of items the systemhave to be aware of is susceptibility high.

Recent research have studied the use of such neural networks at a user’s session level.This thesis rather examines the use of this technique at a whole user’s past history levelassociated with techniques such as embeddings and softmax sampling in order to accommodatewith the high dimensionality.

The proposed method results in a sequence prediction model that can be used as is forthe recommender task or as a feature within a more complex system.

Abstract [sv]

De Recurrent Neural Networks har möjlighet att förstå de tidsmässiga mönstren inom data. Det här är en egenskap som kan användas för att hjälpa ett rekommendatörsystem bättre med hänsyn till användarens historia. Problemet med dimensioner inom rekommendatörsystem uppstår dock även här, eftersom antalet saker som systemet måste vara medveten om är extremt många.

Nyare forskning har studerat användningen av sådana neurala nätverk på en användaressessionsnivå. Denna avhandling undersöker snarare användningen av denna teknik som en hel användares tidigare historiknivå i samband med tekniker som inbäddning och softmax-provtagning för att tillgodose den höga dimensionen.

Den föreslagna metoden resulterar i en sekvensprediktionsmodell som kan användas som för recommender-uppgiften eller som en funktion inom ett mer komplext system.

Place, publisher, year, edition, pages
2017. , 49 p.
Keyword [en]
rnn recommender system
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-216584OAI: oai:DiVA.org:kth-216584DiVA: diva2:1151407
External cooperation
Rexel France
Educational program
Master of Science - Machine Learning
Supervisors
Examiners
Available from: 2017-11-06 Created: 2017-10-23 Last updated: 2017-11-06Bibliographically approved

Open Access in DiVA

fulltext(594 kB)8 downloads
File information
File name FULLTEXT01.pdfFile size 594 kBChecksum SHA-512
93de72ea614231f49ce6fe075ce53d13760af561f8e1f07437f5530202587f4287235e06db4dcf11036346acda5acdd0282992882d5e5812f8e5da7d7ad78e65
Type fulltextMimetype application/pdf

By organisation
School of Computer Science and Communication (CSC)
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 8 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 16 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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