Advertisement

Extracting Naming Concepts by Analyzing Recipes and the Modifiers in Their Titles

  • Shoko Wakamiya
  • Yukiko Kawai
  • Hidetsugu Nanba
  • Kazutoshi Sumiya
Chapter

Abstract

On user-generated recipe-sharing sites such as Rakuten recipe, various modifiers such as “Kid-friendly” and “Simple” are often used in the titles of the recipes to signify their characteristics. Although a modifier is used in a number of recipes’ titles, the underlying concepts utilized vary. In this paper, we propose a system which extract and present Naming Concepts for recipes based on modifiers in their titles. Specifically, the system obtains typical ingredients and cooking utensils by summarizing the recipes for a dish to extract the differences between the elements of recipes and the typical elements in terms of addition, deletion and exchangeability and extracts additional information from procedures. Then, it identifies Naming Concepts for the recipes by extracting feature patterns based on the differences extracted and grouping them on the basis of the patterns. Finally, it presents recipes with granted Naming Concepts for readers. In the experiment, we extract the Naming Concepts of given recipes with a real recipe dataset.

Keywords

Cooking utensils Ingredients Modifiers Naming concepts Recipe features Typical User-generated recipes 

Notes

Acknowledgments

This research was supported in part by Strategic Information and Communications R&D Promotion Programme (SCOPE), the Ministry of Internal Affairs and Communications of Japan and JSPS KAKENHI Grant Number 26280042. The experimental Rakuten recipe dataset was provided by Rakuten Data Release from the Rakuten, Inc.

References

  1. 1.
  2. 2.
  3. 3.
    A. Tachibana, S. Wakamiya, H. Nanba, K. Sumiya, Extraction of naming concepts based on modifiers in recipe titles, in Proceedings of the International MultiConference of Engineers and Computer Scientists 2014, IMECS 2014, 12–14 Mar 2014, Hong Kong. Lecture Notes in Engineering and Computer Science, pp. 507–512Google Scholar
  4. 4.
    C.-Y. Teng, Y.-R. Lin, L.A. Adamic, Recipe recommendation using ingredient networks, in Proceedings of the 4th Annual ACM Web Science Conference (WebSci ‘12), pp. 298–307 (2012)Google Scholar
  5. 5.
    M. Ueda, M. Takahata, S. Nakajima, User’s food preference extraction for cooking recipe recommendation, in Proceedings of the 2nd Workshop on Semantic Personalized Information Management: Retrieval and Recommendation, pp. 98–105 (2011)Google Scholar
  6. 6.
    M. Ueda, S. Asanuma, Y. Miyawaki, S. Nakajima, Recipe recommendation method by considering the user’s preference and ingredient quantity of target recipe, in Proceedings of International MultiConference of Engineers and Computer Scientists 2014 (IMECS 2014), pp. 519–523 (2014)Google Scholar
  7. 7.
    A. Yajima, I. Kobayashi, Easy cooking recipe recommendation considering user’s conditions, in Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology—Volume 03 (WI-IAT ‘09), vol. 3, pp. 13–16 (2009)Google Scholar
  8. 8.
    K. Tsukuda, T. Yamamoto, S. Nakamura, K. Tanaka, Plus one or minus one: A method to browse from an object to another object by adding or deleting an element, in Proceedings of the 21st International Conference on Database and Expert Systems Applications, pp. 258–266 (2010)Google Scholar
  9. 9.
    K. Tsukuda, S. Nakamura, T. Yamamoto, K. Tanaka, Typicality analysis of an object and its application to search, in WebDB Forum 2011, 2G-1-2 (2011) (in Japanese)Google Scholar
  10. 10.
    Y. Yamakata, S. Imahori, Y. Sugiyama, S. Mori, K. Tanaka, Feature extraction and summarization of recipes using flow graph, in Proceedings of the 5th International Conference on Social Informatics, LNCS 8238, pp. 241–254 (2013)Google Scholar
  11. 11.
    Y.-J. Chung, Finding food entity relationships using user-generated data in recipe service, in Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 2611–2614 (2012)Google Scholar
  12. 12.
    H. Nanba, Y. Doi, M. Tsujita, T. Takezawa, K. Sumiya, Summarization of multiple cooking recipes, in Proceedings of the 5th Symposium on Wisdom of Crowds, NLC2013-41, vol. 113, no. 338, pp. 39–44 (2013) (in Japanese)Google Scholar
  13. 13.
    L. Lee, Measures of distributional similarity, in Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, pp. 25–32 (1999)Google Scholar
  14. 14.
    D. Lin, Automatic retrieval and clustering of similar words, in Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and the 17th International Conference on Computational Linguistics, pp. 768–774 (1998)Google Scholar
  15. 15.
    R. Takahashi, S. Oyama, H. Ohshima, K. Tanaka, Evaluating truthfulness of modifiers attached to web entity names, in Proceedings of the 11th International Conference on Web-Age Information Management, pp. 429–440 (2010)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Shoko Wakamiya
    • 1
  • Yukiko Kawai
    • 1
  • Hidetsugu Nanba
    • 2
  • Kazutoshi Sumiya
    • 3
  1. 1.Kyoto Sangyo UniversityKyotoJapan
  2. 2.Hiroshima City UniversityHiroshimaJapan
  3. 3.University of HyogoHimejiJapan

Personalised recommendations