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An Euclidean similarity measurement approach for hotel rating data analysis
Dalarna University, School of Technology and Business Studies, Information Systems.ORCID iD: 0000-0003-3681-8173
Dalarna University, School of Technology and Business Studies, Information Systems.
Dalarna University, School of Technology and Business Studies, Information Systems.ORCID iD: 0000-0003-2110-0943
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2017 (English)In: Proceedings 2017 2nd IEEE International Conference on Cloud Computing and Big Data Analysis, ICCCBDA 2017, 2017, p. 293-298Conference paper, Published paper (Refereed)
Abstract [en]

The most widely used method in recommendation systems is collaborative filtering, of which, a critical step is to analyze a user's preferences and make recommendations of products or services based on similarity analysis with other users' ratings. However, collaborative filtering is less usable for recommendation facing the 'cold start' problem, i.e. few comments being given to products or services. To tackle this problem, we propose an improved method that combines collaborative filtering and data classification. We use hotel recommendation data to test the proposed method. The accuracy of the recommendation is determined by the rankings. Evaluations regarding the accuracies of Top-3 and Top-10 recommendation lists using the 10-fold cross-validation method and ROC curves are conducted. The results show that the Top-3 hotel recommendation list proposed by the combined method has the superiority of the recommendation performance than the Top-10 list under the cold start condition in most of the times.

Place, publisher, year, edition, pages
2017. p. 293-298
Keywords [en]
collaborative filtering, ranking systems, recommendation systems, ROC curves
National Category
Information Systems
Research subject
Complex Systems – Microdata Analysis
Identifiers
URN: urn:nbn:se:du-25650DOI: 10.1109/ICCCBDA.2017.7951927ISI: 000414283700054Scopus ID: 2-s2.0-85024390956ISBN: 978-1-5090-4498-6 (print)ISBN: 978-1-5090-4499-3 (electronic)OAI: oai:DiVA.org:du-25650DiVA, id: diva2:1128871
Conference
2nd IEEE International Conference on Cloud Computing and Big Data Analysis, ICCCBDA 2017
Available from: 2017-07-31 Created: 2017-07-31 Last updated: 2018-01-13Bibliographically approved

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Song, William WeiForsman, AndersAvdic, AndersÅkerblom, Leif

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • 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