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

  • Wei Chen
  • Xiangyu ZhaoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9645)

Abstract

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.

Keywords

Recipe data Classification User-generated contents Semi-structured data Non-structured data 

Notes

Acknowledgement

This work is supported by Fundamental Research Funds of Agricultural Information Institute, Chinese Academy of Agricultural Sciences (No. 2014-J-011), and Project of Ministry of Agriculture of China “Agricultural information monitoring and early-warning”.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Agricultural Information InstituteChinese Academy of Agricultural SciencesBeijingChina
  2. 2.Beijing Research Center for Information Technology in AgricultureBeijingChina
  3. 3.Key Laboratory of Agri-information Service TechnologyMinistry of AgricultureBeijingChina
  4. 4.National Engineering Research Center for Information Technology in AgricultureBeijingChina

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