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A Design of Group Recommendation Mechanism Considering Opportunity Cost and Personal Activity Using 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.461, pp.289-298
Keyword
Group recommendationHadoopLeast MiseryOpportunity costSpark
Mesh Keyword
Average methodGroup membersGroup recommendationsHadoopHigh-accuracyLeast MiseryOpportunity costsRecommendation accuracy
All Science Classification Codes (ASJC)
Industrial and Manufacturing Engineering
Abstract
Group Recommendation is a method of recommending a specific item (e.g. product, service) to a group formed of several members. Least Misery, one of the representative group recommendation method, can recommend items considering group dissatisfaction, but has a drawback of low recommendation accuracy and the other method, Average methods has high accuracy but cannot consider the group dissatisfaction. In this paper, we developed a group recommendation method that improves the recommendation accuracy by measuring the Opportunity Cost (Opportunity Cost is the largest value of the remaining item that is discarded when you select a specific item) and personal activity, taking into account group dissatisfaction. Hadoop-Spark Framework was used in the experiment to distribute large scale of data safely and process efficiently. In Experiment result the proposed group recommendation method improved the recommendation accuracy by 27% while considering the dissatisfaction of the group members compared to the Least Misery method.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36249
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85032509980&origin=inward
DOI
https://doi.org/10.1007/978-981-10-6520-0_32
Journal URL
http://www.springer.com/series/7818
Type
Conference
Funding
This work is supported by Ajou University Research Fund.
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Park, Kiejin  Image
Park, Kiejin 박기진
Department of Industrial Engineering
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