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Similarity-Based Classification for Big Non-Structured and Semi-Structured Recipe Data

文献类型： 外文期刊

作者： Chen, Wei 1 ; Zhao, Xiangyu 2 ;

作者机构： 1.Chinese Acad Agr Sci, Agr Informat Inst, Beijing, Peoples R China

2.Beijing Res Ctr Informat Technol Agr, Beijing, Peoples R China

3.Minist Agr, Key Lab Agriinformat Serv Technol, Beijing, Peoples R China

4.Natl Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China

关键词： Recipe data;Classification;User-generated contents;Semi-structured data;Non-structured data

期刊名称：DATABASE SYSTEMS FOR ADVANCED APPLICATIONS, DASFAA 2016 （ 影响因子：0.302； ）

ISSN： 0302-9743

年卷期： 2016 年 9645 卷

页码：

收录情况： SCI

摘要： In current big data era, there has been an explosive growth of various data. Most of these large volume of data are non-structured or semi-structured (e.g., tweets, weibos or blogs), which are difficult to be managed and organized. Therefore, an effective and efficient classification algorithm for such data is essential and critical. In this article, we focus on a specific kind of non-structured/semi-structured data in our daily life: recipe data. Furthermore, we propose the document model and similarity-based classification algorithm for big non-structured and semi-structured recipe data. By adopting the proposed algorithm and system, we conduct the experimental study on a real-world dataset. The results of experiment study verify the effectiveness of the proposed approach and framework.

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