Scalable recommendation systems based on finding similar items and sequences
Yükleniyor...
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Wiley
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The 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.
Açıklama
Anahtar Kelimeler
Airline Ancillary Services, Apache Spark, Association Rulemining, Distributed Systems, Sequential pattern mining
Kaynak
Concurrency and Computation: Practice and Experience
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
34
Sayı
20
Künye
Uzun‐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
