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Improving top-K contents recommendation performance by considering Bandwagon effect: Using Hadoop-Spark framework
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Publication Year
2018-01-01
Journal
Lecture Notes in Electrical Engineering
Publisher
Springer Verlag
Citation
Lecture Notes in Electrical Engineering, Vol.474, pp.137-142
Keyword
Apache SparkBandwagon effectHadoopTop-K contents
Mesh Keyword
Bandwagon effectCollaborative filtering recommendationsContents recommendationsHadoopHigh-speed operationRecommendation accuracyTop-K contentsTop-K recommendations
All Science Classification Codes (ASJC)
Industrial and Manufacturing Engineering
Abstract
The study on the existing Collaborative filtering recommendation system is mainly aimed at improving the accuracy of prediction. However, in terms of actual recommendation service, it is more important that the Top-K recommendation list, which is effectively recommended to the user, is an item that the user actually likes, rather than improving the recommendation accuracy of all items. In this paper, we have developed a recommendation system that considers the psychological concept of Bandwagon Effect in order to improve the recommendation accuracy of the Top-K contents. For Big data distribution and storage, we used Hadoop and for the fast Big Data processing offering speed, we used Spark, an in-memory data processing framework for high-speed operations. As a result, the proposed model is superior to the existing model in terms of accuracy of recommendation for Top-K contents.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36255
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85039419925&origin=inward
DOI
https://doi.org/10.1007/978-981-10-7605-3_23
Journal URL
http://www.springer.com/series/7818
Type
Conference
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Park, Kiejin  Image
Park, Kiejin 박기진
Department of Industrial Engineering
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