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Efficient Features for Movie Recommendation Systems
KTH, School of Electrical Engineering (EES), Communication Theory.
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

User written movie reviews carry substantial amounts of movie related

features such as description of location, time period, genres, characters,

etc. Using natural language processing and topic modeling based

techniques, it is possible to extract features from movie reviews and find

movies with similar features. In this thesis, a feature extraction method

is presented and the use of the extracted features in finding similar

movies is investigated. We do the text pre-processing on a collection of

movie reviews. We then extract topics from the collection using topic

modeling techniques and store the topic distribution for each movie.

Similarity metrics such as Hellinger distance is then used to find movies

with similar topic distribution. Furthermore, the extracted topics are

used as an explanation during subjective evaluation. Experimental results

show that our extracted topics represent useful movie features and

that they can be used to find similar movies efficiently.

Place, publisher, year, edition, pages
2014. , 53 p.
Series
EES Examensarbete / Master Thesis, XR-EE-KT 2014:012
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-155137OAI: oai:DiVA.org:kth-155137DiVA: diva2:759691
External cooperation
Vionlabs AB, Stockholm
Presentation
(English)
Supervisors
Examiners
Available from: 2014-11-05 Created: 2014-10-30 Last updated: 2014-11-05Bibliographically approved

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CiteExportLink to record
Permanent link

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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
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