Surprising Recipe Extraction based on Rarity and Generality of Ingredients

Kyosuke Ikejiri, Yuichi Sei, Hiroyuki Nakagawa, Yasuyuki Tahara, Akihiko Ohsuga

2014

Abstract

Many surprising recipes that utilize different ingredients or cooking processes from normal recipes exist on user-generated recipe sites. The easiest way to find surprising recipes is to use the search function of the recipe sites. However, the titles of surprising recipes do not always include a keyword, such as “surprise”, or an indication that a recipe is unusual in any way. Therefore, we cannot find surprising recipes very easily. In this paper, we propose a method to extract surprising or unique recipes from those user-generated recipe sites. We propose an RF-IIF (Recipe Frequency-Inverse Ingredient Frequency) based on TF-IDF (Term Frequency- Inverse Ingredient Frequency). First, we calculate the surprising value of the ingredients by using RF-IIF. Then, we calculate the surprising value of each recipe by summing the surprising values of the ingredients that appear in a recipe. Finally, we extract recipes that have high surprising values as surprising recipes of the dish category. In the evaluation experiment, the subjects requested an evaluation about each surprising recipe. As a result, we showed that the extracted recipes were valid recipes and also had a surprising or unusual element. Therefore, we showed the usefulness of the proposed method.

References

  1. Young-J. C., 2012. Finding Food Entity Relationships using User-generated Data in Recipe Service. In CIKM 2012. Proceedings of the 21st ACM international conference on Information and knowledge management.
  2. Maarten C., Arjen P. de V., Marcel J. T. R., 2008. Detecting Synonyms in Social Tagging Systems to Improve Content Retrieval. In SIGIR 2008. Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval.
  3. Jiaul H. P., 2013. A Novel TF-IDF Weighting Scheme for Effective Ranking. In SIGIR 2013. Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval.
  4. Luis F. S. T., Gabriel P. L., Rita A. R., 2012. An Extensive Comparison of Metrics for Automatic Extraction of Key Terms. In ICAART 2012. Proceedings of the 4th International Conference on Agents and Artificial Intelligence.
  5. Senthil M., Rose C., Vibha S. S., Avinava D., 2012. AUSUM: Approach for Unsupervised Bug Report SUMmarization. In FSE 2012. Proceedings of the ACM SIGSOFT 20th International Symposium on the Foundations of Software Engineering.
  6. Ueda M., Ishihara K., Hirano Y., Kajita S., Mase K., 2008. Recipe Recommendation Method Based on Personal Use History of Foodstuff to Reflect Personal Preference. In Database Society of Japan Letters Vol.6 No.4, pp.29-32. (in Japanese).
  7. Ueda M., Takahata M., Nakajima S., 2011. User's Food Preference Extraction for Personalized Cooking Recipe Recommendation. In SPIM 2011. 2nd Workshop on Semantic Personalized Information Management: Retrieval and Recommendation.
  8. Maruyama T., Kawano Y., Yanai K., 2012. Real-time Mobile Recipe Recommendation System Using Food Ingredient Recognition. In IMMPD 2012. Proceeding of the 2nd ACM international workshop on Interactive multimedia on mobile and portable devices.
  9. Yajima A., Kobayashi I., 2009. “Easy” Cooking Recipe Recommendation Considering User's Conditions. In WI-IAT 2009. Proceedings of The 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent TechnologyVolume03.
  10. Akazawa Y., Miyamori H., 2012. Proposal of a Search System for Cooking Recipes Considering Ingredients in Refrigerators. In DEIM 2012. The 4th Forum on Data Engineering and Information Management. (in Japanese).
  11. Kitamura K., Yamasaki T., Aizawa K., 2009. FoodLog: capture, analysis and retrieval of personal food images via web. In CEA 2009. Proceedings of the ACM multimedia 2009 workshop on Multimedia for cooking and eating activities.
  12. Tagawa Y., Tanaka A., Minami Y., Namikawa D., Shimomura M., Ymaguchi T., 2013. Supporting Diet Therapy Based on Japanese Linked Data. In JSAI 2013. The 27th Annual Conference of the Japanese Society for Artificial Intelligence. (in Japanese).
  13. Youri van P., Gijs G., Paul K., 2011. Deriving a Recipe Similarity Measure for Recommending Healthful Meals. In IUI 2011. Proceedings of the 16th international conference on Intelligent user interfaces.
  14. Jill F., Shlomo B., 2010. Intelligent Food Planning: Personalized Recipe Recommendation. In IUI 2010. Proceedings of the 15th international conference on Intelligent user interfaces.
  15. Karasawa T., Hamada R., Ide I., Sakai S., Tanaka H., 2004. Extraction of Knowledge about the Material and Recipe from Cooking Teaching Materials Text. In IPSJ 2004. The 66th Annual Conference of the Information Processing Society of Japan. (in Japanese).
  16. Shidochi Y., Takahashi T., Ide I., Murase H., 2009. Finding Replaceable Materials in Cooking Recipe Texts Considering Characteristic Cooking Actions. In CEA 2009. Proceedings of the ACM multimedia 2009 workshop on Multimedia for cooking and eating activities.
  17. Chun-Y. T., Yu-R. L., Lada A. A., 2012. Recipe recommendation using ingredient networks. In WebSci 2012. Proceedings of the 3rd Annual ACM Web Science Conference.
  18. Fang-Fei K., Cheng-Te L., Man-Kwan S., Suh-Yin L., 2012. Intelligent menu planning: recommending set of recipes by ingredients. CEA 2012. Proceedings of the ACM multimedia 2012 workshop on Multimedia for cooking and eating activities.
  19. Murakami T., Mori K., Orihara R., 2008. Metrics for Evaluating the Serendipity of Recommendation Lists. In JSAI 2007. Proceedings of the 2007 conference on New frontiers in artificial intelligence.
  20. Yuan C. Z., Diarmuid O S., Danele Q., Tamas J., 2012. Auralist: Intoducing Serendipity into Music Recommendation. In WSDM 2012. Proceedings of the fifth ACM international conference on Web search and data mining.
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Paper Citation


in Harvard Style

Ikejiri K., Sei Y., Nakagawa H., Tahara Y. and Ohsuga A. (2014). Surprising Recipe Extraction based on Rarity and Generality of Ingredients . In Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-015-4, pages 428-436. DOI: 10.5220/0004817304280436


in Bibtex Style

@conference{icaart14,
author={Kyosuke Ikejiri and Yuichi Sei and Hiroyuki Nakagawa and Yasuyuki Tahara and Akihiko Ohsuga},
title={Surprising Recipe Extraction based on Rarity and Generality of Ingredients},
booktitle={Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2014},
pages={428-436},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004817304280436},
isbn={978-989-758-015-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Surprising Recipe Extraction based on Rarity and Generality of Ingredients
SN - 978-989-758-015-4
AU - Ikejiri K.
AU - Sei Y.
AU - Nakagawa H.
AU - Tahara Y.
AU - Ohsuga A.
PY - 2014
SP - 428
EP - 436
DO - 10.5220/0004817304280436