2 1 2 Summary Nowadays, blog type recipe portal site such as Recipe blog and user-generated recipe sites such as Cookpad become popular. It is easy fo

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1 No. 178 A Clustering Method for Extracting Closely Similar Recipes in User-generated Recipe Sites

2 2 1 2 Summary Nowadays, blog type recipe portal site such as Recipe blog and user-generated recipe sites such as Cookpad become popular. It is easy for users to post and browse the information of food and recipes. For example, Recipe blog users post information such as trivia and health food. In the case of the Cookpad, the content is consists of ingredients list, images and cooking directions. Recipe cites are generated by many people, than much information exists on the recipe cites. Therefore, it is difficult to comprehend recipes. We propose two methods of how to extract recipe information from the internet. First, recently, people concern about food for the health-conscious is heightened. Therefore, it is easy for users to get the information of food for the health-conscious. However, these recipes may not be usually food, such as smoothie and potage. Therefore, we propose a method to extract alternative ingredients for health-conscious. Second, deliberately or accidentally, numerous closely similar recipes are posted among the user-generated recipes. These recipes cause information overload. In fact, they impede user s recipe searches. We proposed a clustering method to extract closely similar recipes in user generated recipe sites. We propose a method to extract alternative ingredients of health-conscious and closely similar recipes from recipe cites. Therefore, it becomes easy to the user s recipe search.

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38 , Vol. 8, No. 2, pp , (DEIM2015) (DEIM2016) 2016 (to appear) Shunsuke Hanai, Hidetsugu Nanba, Akiyo Nadamoto, Clustering for Closely Similar Recipes to Extract Spam Recipes in User-generated Recipe Sites The 17th International Conference on Information Integration and Web-based Applications & Services(iiWAS 15), December 11-13, Brussels, Belgium, pp , (SIG-DBS) 2014 [1] pp [2] Shidochi, Y., Takahashi, T., Ide, I. and Murase, H. Finding replaceable materials in cooking recipe texts considering characteristic cooking actions, Proc. ACM multimedia 2009 workshop on Multimedia for cooking and eating activities, pp. 9-14, [3], vol.113, no.214, DE , pp ,

39 [4] word2vec vol.114 no. 204 DE pp [5] Teng, C., Lin, Y. and Adamic, L. A. Recipe recommendation using ingredient networks, Proc. 4th International Conference on Web Science, pp , [6] Forbes, P. and Zhu, M. Content-boosted matrix factorization for recommender systems: experiments with recipe recommendation, Proc. 5th ACM conference on Recommender systems, pp , [7] WWW [8] Vol.8 No.4 pp [9] Geleijnse, G., Nachtigall, P., van Kaam, P. and Wijgergangs, L. A personalized recipe advice system to promote healthful choices, Proc. 16th international conference on Intelligent user interfaces, pp , [10] Pinxteren, Y. V., Geleijnse, G. and Kamsteeg, P. Deriving a recipe similarity measure for recommending healthful meals, Proc. 16th international conference on Intelligent user interfaces, pp , [11],, 4 D9-2, [12], 14 pp [13] Wang, L., Li, Q., Li, N., Li, G. and Yang, Y. Substructure similarity measurement in chinese recipes, Proc. 17th International Conference on World Wide Web, pp , [14] Li, Q., Chen, W. and Yu, L. Community-based recipe recommendation and adaptation in peer-to-peer networks, Proc. 4th International Conference on Uniquitous Information Management and Communication, pp. 18:1-18:6, [15] Mori, S., Sasada, T., Yamakata, Y. and Yoshino, K. A machine learning approach to recipe text processing, Proc. 1st Cooking with Computer Workshop, pp , [16] Kuo, F., Li, C., Shan, M. and Lee, S. Intelligent menu planning: recommending set of recipes by ingredients, Proc. ACM multimedia 2012 workshop on Multimedia for cooking and eating activities, pp. 1-6, [17] Yamakata, Y., Imahori, S., Sugiyama, Y., Mori, S. and Tanaka, K. Feature extraction and summarization of recipes using flow graph, Proc. 5th International Conference on Social Informatics, pp ,

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