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Rekommendationssystem för livestreamingtjänster
KTH, School of Information and Communication Technology (ICT).
2016 (Swedish)Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesis
Abstract [sv]

Användningen och behovet av rekommendationssystem i digitala tjänster har växt i takt med att utbudet i dessa blivit allt större och svårare för användare att navigera i. Rekommendationssystem används idag i allt ifrån E-handel till musikoch filmstreaming. För att förse användare med rekommendationer på objekt används en mängd olika väl beprövade algoritmer, filtreringsmetoder och datainsamlingsmetoder. Att applicera dessa i livestreamingtjänster ställer nya krav på systemen eftersom innehållet byts ut mer frekvent, helt nytt innehåll tillkommer regelbundet och explicit data samt metadata är sällan tillräcklig för att ta fram träffsäkra rekommendationer.

I en fallstudie med företaget Liveguide undersöks hur rekommendationssystem kan appliceras i livestreamingtjänster med avseende på de utmaningar och krav som finns. Metoder presenteras där aktuella lösningar testas, utvärderas och anpassas till att fungera bra i livestreamingsammanhang. Slutligen föreslås tre modeller för rekommendationssystem som tagits fram utifrån det resultat metoderna leder till. För att tillfredsställa de identifierade utmaningarna inom området visade sig hybrida, mångsidiga modeller fördelaktiga i livestreaming.

Abstract [en]

The usage and demand of recommender systems in digital services has increased in line with their huge range of products, making it more difficult for users to navigate through the content. Recommender systems are used in a wide scope of digital services ranging from E-commerce to music and film streaming. In order to provide users with recommendations on objects, a variety of algorithms, filtering methods and methods of data collections are being used. Applying these in live streaming services puts new demands on such systems since the content is replaced frequently and new objects added regularly. Furthermore, livestreaming services often lack explicit data and metadata, making recommendations less accurate.

In a case study with Liveguide, recommender systems are evaluated, focusing on whether they are applicable to live streaming services, respecting requirements and demands on such systems. Methods are presented which tests, evaluates and adapts existing solutions to fit in well in context of live streaming. Finally, three models for recommender systems are suggested, based on the methods result. In order to satisfy the identified challenges, hybrid models turned out to be preferable in the context.

Place, publisher, year, edition, pages
2016. , 56 p.
Series
TRITA-ICT-EX, 2016:27
Keyword [en]
Recommender systems, live streaming, algorithms, filtering methods
Keyword [sv]
Rekommendationssystem, livestreaming, algoritmer, filtreringsmetoder
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-204954OAI: oai:DiVA.org:kth-204954DiVA: diva2:1088215
Subject / course
Computer Technology and Real Time Programming
Educational program
Bachelor of Science in Engineering - Computer Engineering
Supervisors
Examiners
Available from: 2017-04-13 Created: 2017-04-11 Last updated: 2017-04-21Bibliographically approved

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fulltext(781 kB)10 downloads
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2f2b9fa184aa8f1988965382d79760c32a9ba769620a02153b703cc88082b1dff0dbc832c8f44734cbaab443c1f9c298ed757f2e474a1043bb500b4ef7c43fdc
Type fulltextMimetype application/pdf

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CiteExportLink to record
Permanent link

Direct link
Cite
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