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Comparison and improvement of time aware collaborative filtering techniques: Recommender systems
Linköping University, Department of Computer and Information Science.
Linköping University, Department of Computer and Information Science.
2019 (English)Independent thesis Basic level (degree of Bachelor), 10,5 credits / 16 HE creditsStudent thesisAlternative title
Jämförelsestudie och förbättring av tidsmedvetna kollaborativa filtreringstekniker : Rekommendationssystem (Swedish)
Abstract [en]

Recommender systems emerged in the mid '90s with the objective of helping users select items or products most suited for them. Whether it is Facebook recommending people you might know, Spotify recommending songs you might like or Youtube recommending videos you might want to watch, recommender systems can now be found in every corner of the internet. In order to handle the immense increase of data online, the development of sophisticated recommender systems is crucial for filtering out information, enhancing web services by tailoring them according to the preferences of the user. This thesis aims to improve the accuracy of recommendations produced by a classical collaborative filtering recommender system by utilizing temporal properties, more precisely the date on which an item was rated by a user. Three different time-weighted implementations are presented and evaluated: time-weighted prediction approach, time-weighted similarity approach and our proposed approach, weighting the mean rating of a user on time. The different approaches are evaluated using the well known MovieLens 100k dataset. Results show that it is possible to slightly increase the accuracy of recommendations by utilizing temporal properties.

Place, publisher, year, edition, pages
2019. , p. 33
Keywords [en]
recommender systems, machine learning, collaborative filtering, movielens
Keywords [sv]
Rekommendationssystem, maskininlärning
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:liu:diva-160360ISRN: LIU-IDA/LITH-EX-G--2019/023--SEOAI: oai:DiVA.org:liu-160360DiVA, id: diva2:1352791
External cooperation
Cybercom Jönköping
Subject / course
Computer Engineering
Supervisors
Examiners
Available from: 2019-09-20 Created: 2019-09-19 Last updated: 2019-09-20Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • de-DE
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  • en-US
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  • Other locale
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Output format
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