Mashup recommendation model for trigger action programming paradigm트리거-액션 프로그래밍 패러다임을 위한 매쉬업 추천 알고리즘

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If This Then That (IFTTT) is a popular platform that deploys mashed-up applications for end users using trigger-action programming (TAP) paradigm. To date, there are about 135 thousand mashup creators who have shared mashup applications using TAP, and around 24 million mashups have been reused by IFTTT users. Up to this date, existent research has not focused on using mashups as a content for recommendations. In this work, we propose a model for mashup recommendation for Trigger Action Programming. For that purpose we propose rating strategies for unrated mashups based on the mashup community dynamics. Then, for the purpose of recommendation, we propose a series of implicit factor features unique to the TAP environment. Finally, we test our approach using various recommendation algorithms using the 200,000 recipes dataset from the IFTTT platform and compared its performance for content recommendation.
Advisors
Ko, In Youngresearcher고인영researcher
Description
한국과학기술원 :전산학부,
Publisher
한국과학기술원
Issue Date
2018
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전산학부, 2018.8,[iv, 38 p. :]

Keywords

Recommendation systems▼amashup recommendation▼atrigger-action programming; 추천 시스템▼a트리거-액션 프로그래밍▼a매시업 추천

URI
http://hdl.handle.net/10203/267001
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=828612&flag=dissertation
Appears in Collection
CS-Theses_Master(석사논문)
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