한국정보처리학회:학술대회논문집 (Proceedings of the Korea Information Processing Society Conference)
- 한국정보처리학회 2009년도 춘계학술발표대회
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- Pages.834-837
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- 2009
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- 2005-0011(pISSN)
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- 2671-7298(eISSN)
DOI QR Code
VotingRank: A Case Study of e-Commerce Recommender Application Using MapReduce
- Ren, Jian-Ji (Department of Computer Engineering Dong-A University) ;
- Lee, Jae-Kee (Department of Computer Engineering Dong-A University)
- 발행 : 2009.04.23
초록
There is a growing need for ad-hoc analysis of extremely large data sets, especially at e-Commerce companies which depend on recommender application. Nowadays, as the number of e-Commerce web pages grow to a tremendous proportion; vertical recommender services can help customers to find what they need. Recommender application is one of the reasons for e-Commerce success in today's world. Compared with general e-Commerce recommender application, obviously, general e-Commerce recommender application's processing scope is greatly narrowed down. MapReduce is emerging as an important programming model for large-scale data-parallel applications such as web indexing, data mining, and scientific simulation. The objective of this paper is to explore MapReduce framework for the e-Commerce recommender application on major general and dedicated link analysis for e-Commerce recommender application, and thus the responding time has been decreased and the recommender application's accuracy has been improved.
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