Abstract
There are many recipes that are regularly shared on the Internet, and they comprise features such as ingredients, cooking procedures, and cooking time. However, it is sometimes difficult to understand the recipes exactly because they are not always written quantitatively and objectively. In order to preserve the accuracy of the recipes, an objective approach is required. In this paper, we define the quantitative characteristics of recipes and determine the relation between them. We use the ingredients of recipes to define their characteristics. We define each of the ingredients based on the statistical entropy measure. In addition, we develop a recipe-transition graph, and we define the asymmetric entropy difference by considering the difference in the ingredients of two recipes as the transition effort. We then show the utilization of this recipe-transition graph. The experimental results show that the most transitive recipes and the recipe transition path can be found with the recipe-transition graph.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ahnert, S.E.: Generalized power graph compression reveals dominant relationship patterns in complex networks. Scientific Reports 4(4385). Nature Publishing Group (2014)
Geleijnse, G., Nachtigall, P., Kaam, P.V., Wijgergangs, L.: A personalized recipe advice system to promote healthful choices. In: Proc. of 16th Intelligent User Interfaces, pp. 437–438. ACM, New York (2011)
Freyne, J., Berkovsky, S.: Intelligent food planning: personalized recipe recommendation. In: Proc. of Intelligent User Interfaces, pp. 221–324 (2010)
Kuo, F., Li, C., Shan, M., Lee, S.: Intelligent menu planning: recommending set of recipes by ingredients. In: Proc. of the ACM Multimedia Workshop on Multimedia for Cooking and Eating Activities, pp. 1–6 (2012)
Teng, C., Lin, Y., Adamic, L.: Recipe recommendation using ingredient networks. In: Proc. of 4th Annual ACM Web Science Conference, pp. 298–307. ACM, New York (2012)
Wang, L., Li, Q., Li, N., Dong, G., Yang, Y.: Substructure similarity measurement in Chinese recipes. In: Proc. of World Wide Web, pp. 979–988 (2008)
Ahn, Y.Y., Ahnert, S.E., Bagrow, J.P., Barabasi, A.-L.: Flavor network and the principles of food pairing. Nature Scientific Reports (2011)
Ueda, M., Asanuma, S., Miyawaki, Y., Nakajima, S.: Recipe recommendation method by considering the user’s preference and ingredient quantity of target recipe. In: Proc. of the International Multi-Conference of Engineers and Computer Scientists, vol. 1, pp. 12–14 (2014)
Kim, S., Lee, Y., Kang, S.H., Cho, H., Yoon, S.: Constructing Cookery Network based on Ingredient Entropy Measure. Indian Journal of Science and Technology 8(23), 1–9 (2015)
Shidochi, Y., Takahashi, T., Ide, I.: Finding replaceable materials in cooking recipe texts considering characteristic cooking actions. In: Proc. of 9th ACM Multimedia Workshop on Multimedia for Cooking and Eating Activities, pp. 9–14. ACM, New York (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Park, J., Kim, SD., Lee, YJ., Cho, HG. (2016). Recipe-Transition Graph Based on Asymmetric Entropy Difference. In: Kim, K., Joukov, N. (eds) Information Science and Applications (ICISA) 2016. Lecture Notes in Electrical Engineering, vol 376. Springer, Singapore. https://doi.org/10.1007/978-981-10-0557-2_17
Download citation
DOI: https://doi.org/10.1007/978-981-10-0557-2_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-0556-5
Online ISBN: 978-981-10-0557-2
eBook Packages: EngineeringEngineering (R0)