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題名 由食譜資料探勘分析料理的在地化：以韓式料理為例
Discovery and Comparative Analysis on the Localization of Cuisine from Recipe Datasets: An Analysis of Korean Cuisine in Taiwan作者 柳桓任
Ryu, Hwan-Im貢獻者 沈錳坤
Shan, Man-Kuan
柳桓任
Ryu, Hwan-Im關鍵詞 食譜探勘
飲食文化
在地化
Recipe Mining
Food Culture
Localization日期 2021 上傳時間 1-Nov-2021 11:59:02 (UTC+8) 摘要 隨著全球化的影響，社群媒體的發達，各地區的文化交流更加頻繁。各地區 的料理文化也隨著全球化擴展到不同的地區，形成料理的在地化。從食譜網站獲取食譜資料，並分析每個地區料理的特徵樣式，可幫助我們了解每個地區料理的特色。目前有許多研究探討不同地區的料理樣式，但卻沒有料理在地化的研究。因此本論文將研究料理的在地化。本論文提供了料理的在地化比較分析的架構，並以在台灣的韓式料理為例，比較分析韓國的韓式料理在台灣在地化的變化，包括常見食材，核心食材，食材搭配關係圖，食材搭配關聯規則，熱門食譜代表食材。本論文的實驗由台灣與韓國的韓式料理食譜中，發現有趣的獨特樣式。
With the development of globalization and the maturity of social media technology, culture exchange between regions becomes more frequent. Cooking cultures have spread over regions with globalization.Obtaining recipe information from the recipe website and analyzing the cuisine in each region is helpful for understanding the cooking styles of each region. Current studies focus on the discovery of cooking styles over different cuisine. To the best of our knowledge, none has devoted to the discovery of and comparative analysis on the localization of cuisine from recipe datasets. This thesis proposed a framework for comparative analysis of localization of cuisine, and analyzed the Korean cuisine in Taiwan as an example to discover and compare the frequent ingredients, the core ingredients, the ingredient pairing and ingredient association rules. The experiment discovered some interesting distinguished patterns of Korean cuisine between Taiwan and Korea.參考文獻 [1] Yu-Xiao Zhu, Junming Huang, Zi-Ke Zhang, Qian-Ming Zhang, Tao Zhou, and Yong-Yeol Ahn, Geography and Similarity of Regional Cuisines in China. PLoS ONE, 8(11), 2013.[2] Anupam Jain and Ganesh Bagler, Culinary Evolution Models for Indian Cuisines. Physica A, 503, 2018.[3]Yong-Yeol Ahn, Sebastian E. Ahnert, James P. Bagrow, and Albert-Laszlo Barabasi, Flavor Network and the Principles of Food Pairing. Scientific Reports, 1(196), 2011.[4] Masahiro Kazama, Minami Sugimoto, Chizuru Hosokawa, Keisuke Matsushima, LavR. V arshney and Y oshiki Ishikawa, A Neural Network System for Transformation of Regional Cuisine Style. Front. ICT, 5(14), 2018.[5] Kyung-Joong Kim and Chang-Ho Chung, Tell Me What You Eat, and I Will Tell You Where You Come From: A Data Science Approach for Global Recipe Data on the Web. IEEE Access, 4, 2016.[6] Sina Sajadmanesh, Kissing Cuisines: Exploring Worldwide Culinary Habits on the Web. WWW’17 Companion: Proceedings of the 26rd International Conference on World Wide Web, 2017.[7] Chun-Yuen Teng, Yu-Re Lin and Lada A. Adamic, Recipe Recommendation Using Ingredient Networks. Web Sci’12: Proceedings of the 4th Aunnual ACM Web Science Conference, 2012.[8] Hanna Kicherera, Marcel Dittrichb, Lukas Grebeb, Christian Scheiblec and Roman Klinger, What You Use, Not What You Do: Automatic Classification and Similarity Detection of Recipes. Data & Knowledge Engineering, 117, 2018.[9] Weiqing Min, Shuqiang Jiang, Linhu Liu, Yong Rui, and Ramesh Jain, A Survey on Food Computing. ACM Computing Surveys, 52(5), 2019.[10]Claudia Wagner, Philipp Singer and Markus Strohmaier, The Nature and Evolution of Online Food Preferences. EPJ Data Science, 3(38), 2014.[11] Jermsak Jermsurawong and Nizar Habash, Predicting the Structure of Cooking Recipes. EMNLP’15: Proceedings of the 2015 Conference on Empirical Methods in Natural Languague Processing , 2015.[12]Weiqing Min, Bingkun Bao, Shuhuan Mei, Yaohui Zhu, Yong Rui and Shuqiang Jiang, You Are What You Eat Exploring Rich Recipe Information for Cross-Region Food Analysis. IEEE Transcation on Multimedia, 20(4), 2018.[13]Vladimir Nedovic, Learning Recipe Ingredient Space Using Generative Probabilstic Model. Proceedings of Cooking with Computer Workshop(CwC), 2013.[14]Han Su, Ting-Wei Lin, Cheng-Te Li, Man-Kwan Shan and Janet Chang, Automatic Recipe Cuisine Classification by Ingredients. UbiComp’14 Adjunct: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, 2014.[15] Markus Strohmaier, Markus Strohmaier, and Markus Strohmaier, Mining Cross- Cultural Relations from Wikipedia: A Study of 31 European Food Cultures. Web Sci’15: Proceedings of the ACM Web Science Conference, 2015.[16] Tiago Simas, Michal Ficek, Albert Diazguilera, Pere Obrador, and Pablo R. Rodriguez, Food-Bridging: A New Network Construction to Unveil the Principles of Cooking. Frontiers in ICT, 4(14), 2017.[17]Sofiane Abbar, Yelena Mejova, and Ingmar Weber, You Tweet What you Eat:Studying Food Consumption Through Twitter. CHI’15 Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, 2015.[18] Thiago H Silva, Pedro OS de Melo, Jussara Almeida, Mirco Musolesi, and Antonio Loureiro, You Are What You Eat (and drink): Identifying Cultural Boundaries by Analyzing Food & Drink Habits in Foursquare. ICWSM’14: Proceedings of International Conference on Weblogs and Social Media, 2014.[19] Tomasz Kusmierczyk and Kjetil Norvag, Online Food Recipe Title Semantics: Combining Nutrient Facts and Topics. CIKM’16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 2016.[20] Mikolov Tomas, Kai Chen, Greg Corrado, and Jeffrey Dean, Efficient Estimation of Word Representations in Vector Space. ICLR’13: Proceedings of the International Conference on Learning Representations. 2013.[21] David M. Blei, Andrw Y. Ng, and Michael I. Jordan, Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 2003.[22] Wei Shen, Jianyong Wang and Jiawei Han, Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions. IEEE Transactions on Knowledge and Data Engineering, 27(2), 2015.[23] Minghe Yu, Entity Linking on Graph Data. WWW’14 Companion: Proceedings of the 23rd International Conference on World Wide Web, 2014. 描述 碩士
國立政治大學
資訊科學系
106753040資料來源 http://thesis.lib.nccu.edu.tw/record/#G0106753040 資料類型 thesis dc.contributor.advisor 沈錳坤 zh_TW dc.contributor.advisor Shan, Man-Kuan en_US dc.contributor.author (Authors) 柳桓任 zh_TW dc.contributor.author (Authors) Ryu, Hwan-Im en_US dc.creator (作者) 柳桓任 zh_TW dc.creator (作者) Ryu, Hwan-Im en_US dc.date (日期) 2021 en_US dc.date.accessioned 1-Nov-2021 11:59:02 (UTC+8) - dc.date.available 1-Nov-2021 11:59:02 (UTC+8) - dc.date.issued (上傳時間) 1-Nov-2021 11:59:02 (UTC+8) - dc.identifier (Other Identifiers) G0106753040 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/137671 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學系 zh_TW dc.description (描述) 106753040 zh_TW dc.description.abstract (摘要) 隨著全球化的影響，社群媒體的發達，各地區的文化交流更加頻繁。各地區 的料理文化也隨著全球化擴展到不同的地區，形成料理的在地化。從食譜網站獲取食譜資料，並分析每個地區料理的特徵樣式，可幫助我們了解每個地區料理的特色。目前有許多研究探討不同地區的料理樣式，但卻沒有料理在地化的研究。因此本論文將研究料理的在地化。本論文提供了料理的在地化比較分析的架構，並以在台灣的韓式料理為例，比較分析韓國的韓式料理在台灣在地化的變化，包括常見食材，核心食材，食材搭配關係圖，食材搭配關聯規則，熱門食譜代表食材。本論文的實驗由台灣與韓國的韓式料理食譜中，發現有趣的獨特樣式。 zh_TW dc.description.abstract (摘要) With the development of globalization and the maturity of social media technology, culture exchange between regions becomes more frequent. Cooking cultures have spread over regions with globalization.Obtaining recipe information from the recipe website and analyzing the cuisine in each region is helpful for understanding the cooking styles of each region. Current studies focus on the discovery of cooking styles over different cuisine. To the best of our knowledge, none has devoted to the discovery of and comparative analysis on the localization of cuisine from recipe datasets. This thesis proposed a framework for comparative analysis of localization of cuisine, and analyzed the Korean cuisine in Taiwan as an example to discover and compare the frequent ingredients, the core ingredients, the ingredient pairing and ingredient association rules. The experiment discovered some interesting distinguished patterns of Korean cuisine between Taiwan and Korea. en_US dc.description.tableofcontents 第 一 章 緒論 81.1 研究背景 81.2 研究動機 91.3 研究目標 10第 二 章 相關研究 11第 三 章 研究方法 143.1 研究架構 143.2 資料來源 143.2.1 iCook.com(愛料理) 153.2.2 10000recipe.com(만개의 레시피) 153.2.3 調味料 173.3 資料前處理 183.3.1 資料清理 183.3.2 同義詞 203.4 實體連結(Entity Linking) 223.5 探勘比較分析 243.5.1 核心食材 243.5.2 關聯規則 243.5.3 食譜演化的比較 25第 四 章 實驗結果 274.1 系統實作 274.2 食材資料集 284.3 常見食材 294.4 常見調味料 304.5 常見非調味料食材 304.6 食材搭配 414.6.1 核心食材 414.6.2 食材搭配網絡圖 434.6.3 關聯規則 464.7 食譜演化的比較結果 49第 五 章 結論與未來研究 51參考文獻 52 zh_TW dc.format.extent 9062504 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0106753040 en_US dc.subject (關鍵詞) 食譜探勘 zh_TW dc.subject (關鍵詞) 飲食文化 zh_TW dc.subject (關鍵詞) 在地化 zh_TW dc.subject (關鍵詞) Recipe Mining en_US dc.subject (關鍵詞) Food Culture en_US dc.subject (關鍵詞) Localization en_US dc.title (題名) 由食譜資料探勘分析料理的在地化：以韓式料理為例 zh_TW dc.title (題名) Discovery and Comparative Analysis on the Localization of Cuisine from Recipe Datasets: An Analysis of Korean Cuisine in Taiwan en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] Yu-Xiao Zhu, Junming Huang, Zi-Ke Zhang, Qian-Ming Zhang, Tao Zhou, and Yong-Yeol Ahn, Geography and Similarity of Regional Cuisines in China. PLoS ONE, 8(11), 2013.[2] Anupam Jain and Ganesh Bagler, Culinary Evolution Models for Indian Cuisines. Physica A, 503, 2018.[3]Yong-Yeol Ahn, Sebastian E. Ahnert, James P. Bagrow, and Albert-Laszlo Barabasi, Flavor Network and the Principles of Food Pairing. Scientific Reports, 1(196), 2011.[4] Masahiro Kazama, Minami Sugimoto, Chizuru Hosokawa, Keisuke Matsushima, LavR. V arshney and Y oshiki Ishikawa, A Neural Network System for Transformation of Regional Cuisine Style. Front. ICT, 5(14), 2018.[5] Kyung-Joong Kim and Chang-Ho Chung, Tell Me What You Eat, and I Will Tell You Where You Come From: A Data Science Approach for Global Recipe Data on the Web. IEEE Access, 4, 2016.[6] Sina Sajadmanesh, Kissing Cuisines: Exploring Worldwide Culinary Habits on the Web. WWW’17 Companion: Proceedings of the 26rd International Conference on World Wide Web, 2017.[7] Chun-Yuen Teng, Yu-Re Lin and Lada A. Adamic, Recipe Recommendation Using Ingredient Networks. Web Sci’12: Proceedings of the 4th Aunnual ACM Web Science Conference, 2012.[8] Hanna Kicherera, Marcel Dittrichb, Lukas Grebeb, Christian Scheiblec and Roman Klinger, What You Use, Not What You Do: Automatic Classification and Similarity Detection of Recipes. Data & Knowledge Engineering, 117, 2018.[9] Weiqing Min, Shuqiang Jiang, Linhu Liu, Yong Rui, and Ramesh Jain, A Survey on Food Computing. ACM Computing Surveys, 52(5), 2019.[10]Claudia Wagner, Philipp Singer and Markus Strohmaier, The Nature and Evolution of Online Food Preferences. EPJ Data Science, 3(38), 2014.[11] Jermsak Jermsurawong and Nizar Habash, Predicting the Structure of Cooking Recipes. EMNLP’15: Proceedings of the 2015 Conference on Empirical Methods in Natural Languague Processing , 2015.[12]Weiqing Min, Bingkun Bao, Shuhuan Mei, Yaohui Zhu, Yong Rui and Shuqiang Jiang, You Are What You Eat Exploring Rich Recipe Information for Cross-Region Food Analysis. IEEE Transcation on Multimedia, 20(4), 2018.[13]Vladimir Nedovic, Learning Recipe Ingredient Space Using Generative Probabilstic Model. Proceedings of Cooking with Computer Workshop(CwC), 2013.[14]Han Su, Ting-Wei Lin, Cheng-Te Li, Man-Kwan Shan and Janet Chang, Automatic Recipe Cuisine Classification by Ingredients. UbiComp’14 Adjunct: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, 2014.[15] Markus Strohmaier, Markus Strohmaier, and Markus Strohmaier, Mining Cross- Cultural Relations from Wikipedia: A Study of 31 European Food Cultures. Web Sci’15: Proceedings of the ACM Web Science Conference, 2015.[16] Tiago Simas, Michal Ficek, Albert Diazguilera, Pere Obrador, and Pablo R. Rodriguez, Food-Bridging: A New Network Construction to Unveil the Principles of Cooking. Frontiers in ICT, 4(14), 2017.[17]Sofiane Abbar, Yelena Mejova, and Ingmar Weber, You Tweet What you Eat:Studying Food Consumption Through Twitter. CHI’15 Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, 2015.[18] Thiago H Silva, Pedro OS de Melo, Jussara Almeida, Mirco Musolesi, and Antonio Loureiro, You Are What You Eat (and drink): Identifying Cultural Boundaries by Analyzing Food & Drink Habits in Foursquare. ICWSM’14: Proceedings of International Conference on Weblogs and Social Media, 2014.[19] Tomasz Kusmierczyk and Kjetil Norvag, Online Food Recipe Title Semantics: Combining Nutrient Facts and Topics. CIKM’16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 2016.[20] Mikolov Tomas, Kai Chen, Greg Corrado, and Jeffrey Dean, Efficient Estimation of Word Representations in Vector Space. ICLR’13: Proceedings of the International Conference on Learning Representations. 2013.[21] David M. Blei, Andrw Y. Ng, and Michael I. Jordan, Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 2003.[22] Wei Shen, Jianyong Wang and Jiawei Han, Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions. IEEE Transactions on Knowledge and Data Engineering, 27(2), 2015.[23] Minghe Yu, Entity Linking on Graph Data. WWW’14 Companion: Proceedings of the 23rd International Conference on World Wide Web, 2014. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202101703 en_US
