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An Euclidean similarity measurement approach for hotel rating data analysis
Högskolan Dalarna, Akademin Industri och samhälle, Informatik.ORCID-id: 0000-0003-3681-8173
Högskolan Dalarna, Akademin Industri och samhälle, Informatik.
Högskolan Dalarna, Akademin Industri och samhälle, Informatik.ORCID-id: 0000-0003-2110-0943
Vise andre og tillknytning
2017 (engelsk)Inngår i: Proceedings 2017 2nd IEEE International Conference on Cloud Computing and Big Data Analysis, ICCCBDA 2017, 2017, s. 293-298Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
2017. s. 293-298
Emneord [en]
collaborative filtering, ranking systems, recommendation systems, ROC curves
HSV kategori
Forskningsprogram
Komplexa system - mikrodataanalys
Identifikatorer
URN: urn:nbn:se:du-25650DOI: 10.1109/ICCCBDA.2017.7951927ISI: 000414283700054Scopus ID: 2-s2.0-85024390956ISBN: 978-1-5090-4498-6 (tryckt)ISBN: 978-1-5090-4499-3 (digital)OAI: oai:DiVA.org:du-25650DiVA, id: diva2:1128871
Konferanse
2nd IEEE International Conference on Cloud Computing and Big Data Analysis, ICCCBDA 2017
Tilgjengelig fra: 2017-07-31 Laget: 2017-07-31 Sist oppdatert: 2018-01-13bibliografisk kontrollert

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Song, William WeiForsman, AndersAvdic, AndersÅkerblom, Leif

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