Scalable recommendation systems based on finding similar items and sequences

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dc.authorid0000-0002-4958-4575en_US
dc.authorscopusid55355863500en_US
dc.authorwosidABH-8073-2020en_US
dc.contributor.authorUzun-Per, Meryem
dc.contributor.authorGürel, Ahmet Volkan
dc.contributor.authorCan, Ali Burak
dc.contributor.authorAktaş, Mehmet S.
dc.date.accessioned2022-01-28T09:23:10Z
dc.date.available2022-01-28T09:23:10Z
dc.date.issued2022en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractThe rapid growth in the airline industry, which started in 2009, continued until the COVID-19 era, with the annual number of passengers almost doubling in 10 years. This situation has led to increased competition between airline companies, whose profitability has decreased considerably. They aimed to increase their profitability by making services like seat selection, excess baggage, Wi-Fi access optional under the name of ancillary services. To the best of our knowledge, there is no recommendation system for recommending ancillary services for airline companies. Also, to the best of our knowledge, there is no testing framework to compare recommendation algorithms considering their scalabilities and running times. In this paper, we propose a framework based on Lambda architecture for recommendation systems that run on a big data processing platform. The proposed method utilizes association rule and sequential pattern mining algorithms that are designed for big data processing platforms. To facilitate testing of the proposed method, we implement a prototype application.We conduct an experimental study on the prototype to investigate the performance of the proposed methodology using accuracy, scalability, and latency related performance metrics. The results indicate that the proposed method proves to be useful and has negligible processing overheads.en_US
dc.identifier.citationUzun‐Per, M., Gürel, A. V., Can, A. B., & Aktaş, M. S. (2022). Scalable recommendation systems based on finding similar items and sequences. Concurrency and Computation: Practice and Experience, 34(20), pp. 1-15. https://doi.org/10.1002/cpe.6841 ‌en_US
dc.identifier.doi10.1002/cpe.6841en_US
dc.identifier.endpage15en_US
dc.identifier.issue20en_US
dc.identifier.scopus2-s2.0-85123074568en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1002/cpe.6841
dc.identifier.urihttps://hdl.handle.net/20.500.13055/148
dc.identifier.volume34en_US
dc.identifier.wosWOS:000744680900001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynak.otherSCI-E - Science Citation Index Expandeden_US
dc.institutionauthorUzun-Per, Meryem
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofConcurrency and Computation: Practice and Experienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAirline Ancillary Servicesen_US
dc.subjectApache Sparken_US
dc.subjectAssociation Ruleminingen_US
dc.subjectDistributed Systemsen_US
dc.subjectSequential pattern miningen_US
dc.titleScalable recommendation systems based on finding similar items and sequencesen_US
dc.typeArticleen_US
dspace.entity.typePublication

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