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題名 根據食材搭配與替代關係設計食譜搜尋的自動完成機制
Autocomplete Mechanism for Recipe Search by Ingredients Based on Ingredient Complement and Substitution
作者 周冠嶔
Chou, Kuan Chin
貢獻者 沈錳坤
Shan, Man Kwan
周冠嶔
Chou, Kuan Chin
關鍵詞 資料採掘
查詢詞自動完成
食譜搜尋引擎
Data Mining
Query Autocomplete
Recipe Search Engine
日期 2016
上傳時間 2-Sep-2016 00:13:50 (UTC+8)
摘要 「民以食為天」，飲食與我們的生活息息相關。近年來由於食安風暴肆虐，自行烹煮的需求隨之高漲。然而在家自行烹煮時常會面臨不知道該烹煮什麼料理的問題，因此有便利的食譜搜尋系統對烹煮的人而言將是相當方便的。然而使用搜尋系統時，由於我們只知道想用某些特定食材進行烹煮，而不知道哪些食譜含有特定食材，因此在以少數食材進行查詢時不免會得到過多的食譜結果而難以快速找到喜好的食譜。我們建立了一個食譜搜尋的自動完成機制，並依照該機制實做出了食譜搜尋引擎。使用者使用系統進行搜尋時，我們將會依照使用者輸入的食材尋找適合搭配的食材推薦給使用者，幫助使用者在查詢時使用更完整的Query讓搜尋系統可以找到更少更精準的食譜，幫助使用者更快的找到喜歡的食譜。然而只推薦搭配性食材，可能會推薦出與Query中的食材是替代關係的食材，也就是通常不會一起出現的食材，因此我們也進行了替代性食材的研究。給定由兩個食材組成的食材配對，我們研究如何自動的判斷替代性食材。我們將問題轉化成分類問題來解決，並使用One-Class Classification的技術解決分類問題中的Imbalanced Problem。我們使用f1-score觀看One-Class Classification與傳統分類器的比較。經實驗測試，One Class Classification與傳統分類器相比，One Class Classification較能協助我們解決Imbalanced Problem。
參考文獻 [1] R. Agrawal and R. Srikant, Fast algorithms for mining association rules. Procedings 20th Internatinal Conference Very Large Data Bases, 1994.
[2] Y. Y. Ahn, S. E. Ahnert, J. P. Bagrow, and A. L. Barabási, Flavor network and the principles of food pairing. Scientific Reports 1, 2011.
[3] S. Amano, K. Aizawa, and M. Ogawa, Food category representatives: extracting categories from meal names in food recordings and recipe data. IEEE International Conference on Multimedia Big Data, 2015.
[4] Z. Bar-Yossef and N. Kraus, Context-sensitive query auto-completion. Proceedings of the 20th International Conference on World Wide Web, 2011.
[5] A. Blansché, J. Cojan, V. Dufour Lussier, J. Lieber, P. Molli, E. Nauer, H. Skaf Molli, and Y. Toussaint, Taaable 3: adaptation of ingredient quantities and of textual preparations. Proceedings of 18th Internatonal Conference on Case-Based Reasoning Workshop, 2010.
[6] D. M. Blei, A. Y. Ng, and M. I. Jordan, Latent dirichlet allocation. Journal of Machine Learning Research.
[7] C. Boscarino, N. J. Koenderink, V. Nedović, and J. L. Top, Automatic extraction of ingredient`s substitutes. Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, 2014.
[8] F. Cai and M. De Rijke, Learning from homologous queries and semantically related terms for query auto completion. Information Processing & Management, 52(4), 2016.
[9] C.-C. Chang and C.-J. Lin, LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3) 2011.
[10] M. De Clercq and W. Waegeman, Prediction of Ingredient Combinations using Machine Learning Techniques. Master Thesis, Ghent University, 2014.
[11] S. C. Deerwester, S. T. Dumais, T. K. Landauer, G. W. Furnas, and R. A. Harshman, Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 1990.
[12] V. Dufour Lussier, J. Lieber, E. Nauer, and Y. Toussaint, Text Adaptation using Formal Concept Analysis. In Case-Based Reasoning. Research and Development, Springer, 2010.
[13] P. Forbes and M. Zhu, Content-Boosted Matrix Factorization for Recommender Systems: Experiments with Recipe Recommendation. Proceedings of the 5th ACM Conference on Recommender Systems, 2011.
[14] J.-Y. Jiang, Y.-Y. Ke, P.-Y. Chien, and P.-J. Cheng, Learning User Reformulation Behavior for Query Auto-Completion. Proceedings of the 37th ACM SIGIR Conference on Research & Development in Information Retrieval, 2014.
[15] S. S. Khan and M. G. Madden, A Survey of Recent Trends in One Class Classification. Irish Conference on Artificial Intelligence and Cognitive Science, 2009.
[16] F. F. Kuo, C. T. Li, M. K. Shan, and S. Y. Lee, Intelligent Menu Planning: Recommending Set of Recipes by Ingredients. Proceedings of the ACM Multimedia Workshop on Multimedia for Cooking and Eating Activities, 2012.
[17] H. Larkin and D. Bridge, Subs and Sandwiches: Replacing One Ingredient by Another. Workshop Programme of the 22nd International Conference on Case-Based Reasoning, 2014.
[18] Z. Liao, D. Jiang, E. Chen, J. Pei, H. Cao, and H. Li, Mining Concept Sequences from Large-Scale Search Logs for Context-Aware Query Suggestion. ACM Transactions on Intelligent Systems and Technology, 3(1), 2011.
[19] A. L. Maas and A. Y. Ng, A Probabilistic Model for Semantic Word Vectors. NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2010.
[20] C. D. Manning, M. Surdeanu, J. Bauer, J. Finkel, S. J. Bethard, and D. Mcclosky, The Stanford Corenlp Natural Language Processing Toolkit. Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 2014.
[21] J. Mcauley, R. Pandey, and J. Leskovec, Inferring Networks of Substitutable and Complementary Products. Proceedings of the 21th ACM International Conference on Knowledge Discovery and Data Mining, 2015.
[22] T. D. Nguyen, D. T. N. Nguyen, and Y. Kiyoki, A Regional Food`s Features Extraction Algorithm and Its Application. Proceedings of the 5th International Workshop on Multimedia for Cooking & Eating Activities, 2013.
[23] Y. Seki and K. Ono, Discriminating Practical Recipes Based on Content Characteristics in Popular Social Recipes. Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, 2014.
[24] Y. Shidochi, T. Takahashi, I. Ide, and H. Murase, Finding Replaceable Materials in Cooking Recipe Texts Considering Characteristic Cooking Actions. Proceedings of the ACM Multimedia Workshop on Multimedia for Cooking and Eating Activities, 2009.
[25] M. Shokouhi and K. Radinsky, Time-Sensitive Query Auto-Completion. Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2012.
[26] T. H. Silva, P. O. De Melo, J. Almeida, M. Musolesi, and A. Loureiro, You are What You Eat (and Drink): Identifying Cultural Boundaries by Analyzing Food & Drink Habits in Foursquare. Proceedings of 8th AAAI International Conference on Weblogs and Social Media, 2014.
[27] D. M. Tax, One-Class Classification. Ph.D Thesis, Delft University of Technology, 2001.
[28] C. Y. Teng, Y. R. Lin, and L. A. Adamic, Recipe Recommendation using Ingredient Networks. Proceedings of the 4th Annual ACM Web Science Conference, 2012.
[29] W. G. Teng, M. J. Hsieh, and M. S. Chen, A Statistical Framework for Mining Substitution Rules. Knowledge and Information Systems, 7(2), 2005.
[30] K. Walter, M. Minor, and R. Bergmann, Workflow Extraction from Cooking Recipes. Proceedings of the International Conference on Case-Based Reasoning Workshops, 2011.
[31] R. West, R. W. White, and E. Horvitz, From Cookies to Cooks: Insights on Dietary Patterns via Analysis of Web Usage Logs. Proceedings of the 22nd International Conference on World Wide Web, 2013.
[32] S. Whiting and J. M. Jose, Recent and Robust Query Auto-Completion. Proceedings of the 23rd International Conference on World Wide Web, 2014.
[33] M. Wiegand, B. Roth, and D. Klakow, Knowledge Acquisition with Natural Language Processing in the Food Domain: Potential and Challenges. Proceedings of the ECAI-Workshop on Cooking with Computers, 2012.
[34] S. Yokoi, K. Doman, T. Hirayama, I. Ide, D. Deguchi, and H. Murase, Typicality Analysis of the Combination of Ingredients in a Cooking Recipe for Assisting the Arrangement of Ingredients. IEEE International Conference on Multimedia & Expo Workshops, 2015.
[35] 呂耀茹, 《由食譜資料探勘料理特徵樣式》. 國立政治大學資訊科學系碩士論文, 2016.
描述 碩士
國立政治大學
資訊科學學系
102753024
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0102753024
資料類型 thesis
dc.contributor.advisor 沈錳坤zh_TW
dc.contributor.advisor Shan, Man Kwanen_US
dc.contributor.author (Authors) 周冠嶔zh_TW
dc.contributor.author (Authors) Chou, Kuan Chinen_US
dc.creator (作者) 周冠嶔zh_TW
dc.creator (作者) Chou, Kuan Chinen_US
dc.date (日期) 2016en_US
dc.date.accessioned 2-Sep-2016 00:13:50 (UTC+8)-
dc.date.available 2-Sep-2016 00:13:50 (UTC+8)-
dc.date.issued (上傳時間) 2-Sep-2016 00:13:50 (UTC+8)-
dc.identifier (Other Identifiers) G0102753024en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/101130-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學學系zh_TW
dc.description (描述) 102753024zh_TW
dc.description.abstract (摘要) 「民以食為天」，飲食與我們的生活息息相關。近年來由於食安風暴肆虐，自行烹煮的需求隨之高漲。然而在家自行烹煮時常會面臨不知道該烹煮什麼料理的問題，因此有便利的食譜搜尋系統對烹煮的人而言將是相當方便的。然而使用搜尋系統時，由於我們只知道想用某些特定食材進行烹煮，而不知道哪些食譜含有特定食材，因此在以少數食材進行查詢時不免會得到過多的食譜結果而難以快速找到喜好的食譜。我們建立了一個食譜搜尋的自動完成機制，並依照該機制實做出了食譜搜尋引擎。使用者使用系統進行搜尋時，我們將會依照使用者輸入的食材尋找適合搭配的食材推薦給使用者，幫助使用者在查詢時使用更完整的Query讓搜尋系統可以找到更少更精準的食譜，幫助使用者更快的找到喜歡的食譜。然而只推薦搭配性食材，可能會推薦出與Query中的食材是替代關係的食材，也就是通常不會一起出現的食材，因此我們也進行了替代性食材的研究。給定由兩個食材組成的食材配對，我們研究如何自動的判斷替代性食材。我們將問題轉化成分類問題來解決，並使用One-Class Classification的技術解決分類問題中的Imbalanced Problem。我們使用f1-score觀看One-Class Classification與傳統分類器的比較。經實驗測試，One Class Classification與傳統分類器相比，One Class Classification較能協助我們解決Imbalanced Problem。zh_TW
dc.description.tableofcontents 第一章 前言 1
1.1研究背景、動機與目的 1
1.2論文架構 4
第二章 相關研究 5
2.1飲食研究 5
2.1.1食譜風格分類 5
2.1.4食譜難易度判斷 5
2.1.2食譜推薦 6
2.1.3食譜創新 6
2.1.5食譜製作流程探勘 6
2.1.6食材分析與搭配 7
2.1.7食材替代 7
2.1.8其他應用 8
2.2食譜搜尋引擎 9
2.3 QUERY AUTO COMPLETION 12
2.3.1 Popularity-based Query Auto Completion 13
2.3.2 Learning-based Query Auto Completion 13
2.4物品替代 14
第三章 研究方法 15
3.1研究說明 15
3.2食譜搜尋的自動完成機制 16
3.3自動判斷替代性食材 19
3.3.1 Phi Coefficient 22
3.3.2 Pointwise Mutual Information 23
3.3.3 Complement Ingredient 24
3.3.4 Cooking Skill 25
3.3.5 Latent Dirichlet Allocation 26
3.3.6 Latent Semantic Analysis 28
第四章 實驗 30
4.1實驗說明 30
4.2資料集介紹 30
4.2.1 Cook’s Thesaurus 31
4.2.2 Allrecipes 33
4.2.3 Yummly 35
4.3前處理 36
4.3.1食材索引整理 36
4.3.2食材同義詞遞移性處理 37
4.4建立替代性食材分類模型 39
4.4.1分類模型評估 40
4.4.2分類模型實驗 41
第五章 結論與未來研究 455
參考文獻 466		zh_TW
dc.format.extent 2184071 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0102753024en_US
dc.subject (關鍵詞) 資料採掘zh_TW
dc.subject (關鍵詞) 查詢詞自動完成zh_TW
dc.subject (關鍵詞) 食譜搜尋引擎zh_TW
dc.subject (關鍵詞) Data Miningen_US
dc.subject (關鍵詞) Query Autocompleteen_US
dc.subject (關鍵詞) Recipe Search Engineen_US
dc.title (題名) 根據食材搭配與替代關係設計食譜搜尋的自動完成機制zh_TW
dc.title (題名) Autocomplete Mechanism for Recipe Search by Ingredients Based on Ingredient Complement and Substitutionen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1] R. Agrawal and R. Srikant, Fast algorithms for mining association rules. Procedings 20th Internatinal Conference Very Large Data Bases, 1994.
[2] Y. Y. Ahn, S. E. Ahnert, J. P. Bagrow, and A. L. Barabási, Flavor network and the principles of food pairing. Scientific Reports 1, 2011.
[3] S. Amano, K. Aizawa, and M. Ogawa, Food category representatives: extracting categories from meal names in food recordings and recipe data. IEEE International Conference on Multimedia Big Data, 2015.
[4] Z. Bar-Yossef and N. Kraus, Context-sensitive query auto-completion. Proceedings of the 20th International Conference on World Wide Web, 2011.
[5] A. Blansché, J. Cojan, V. Dufour Lussier, J. Lieber, P. Molli, E. Nauer, H. Skaf Molli, and Y. Toussaint, Taaable 3: adaptation of ingredient quantities and of textual preparations. Proceedings of 18th Internatonal Conference on Case-Based Reasoning Workshop, 2010.
[6] D. M. Blei, A. Y. Ng, and M. I. Jordan, Latent dirichlet allocation. Journal of Machine Learning Research.
[7] C. Boscarino, N. J. Koenderink, V. Nedović, and J. L. Top, Automatic extraction of ingredient`s substitutes. Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, 2014.
[8] F. Cai and M. De Rijke, Learning from homologous queries and semantically related terms for query auto completion. Information Processing & Management, 52(4), 2016.
[9] C.-C. Chang and C.-J. Lin, LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3) 2011.
[10] M. De Clercq and W. Waegeman, Prediction of Ingredient Combinations using Machine Learning Techniques. Master Thesis, Ghent University, 2014.
[11] S. C. Deerwester, S. T. Dumais, T. K. Landauer, G. W. Furnas, and R. A. Harshman, Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 1990.
[12] V. Dufour Lussier, J. Lieber, E. Nauer, and Y. Toussaint, Text Adaptation using Formal Concept Analysis. In Case-Based Reasoning. Research and Development, Springer, 2010.
[13] P. Forbes and M. Zhu, Content-Boosted Matrix Factorization for Recommender Systems: Experiments with Recipe Recommendation. Proceedings of the 5th ACM Conference on Recommender Systems, 2011.
[14] J.-Y. Jiang, Y.-Y. Ke, P.-Y. Chien, and P.-J. Cheng, Learning User Reformulation Behavior for Query Auto-Completion. Proceedings of the 37th ACM SIGIR Conference on Research & Development in Information Retrieval, 2014.
[15] S. S. Khan and M. G. Madden, A Survey of Recent Trends in One Class Classification. Irish Conference on Artificial Intelligence and Cognitive Science, 2009.
[16] F. F. Kuo, C. T. Li, M. K. Shan, and S. Y. Lee, Intelligent Menu Planning: Recommending Set of Recipes by Ingredients. Proceedings of the ACM Multimedia Workshop on Multimedia for Cooking and Eating Activities, 2012.
[17] H. Larkin and D. Bridge, Subs and Sandwiches: Replacing One Ingredient by Another. Workshop Programme of the 22nd International Conference on Case-Based Reasoning, 2014.
[18] Z. Liao, D. Jiang, E. Chen, J. Pei, H. Cao, and H. Li, Mining Concept Sequences from Large-Scale Search Logs for Context-Aware Query Suggestion. ACM Transactions on Intelligent Systems and Technology, 3(1), 2011.
[19] A. L. Maas and A. Y. Ng, A Probabilistic Model for Semantic Word Vectors. NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2010.
[20] C. D. Manning, M. Surdeanu, J. Bauer, J. Finkel, S. J. Bethard, and D. Mcclosky, The Stanford Corenlp Natural Language Processing Toolkit. Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 2014.
[21] J. Mcauley, R. Pandey, and J. Leskovec, Inferring Networks of Substitutable and Complementary Products. Proceedings of the 21th ACM International Conference on Knowledge Discovery and Data Mining, 2015.
[22] T. D. Nguyen, D. T. N. Nguyen, and Y. Kiyoki, A Regional Food`s Features Extraction Algorithm and Its Application. Proceedings of the 5th International Workshop on Multimedia for Cooking & Eating Activities, 2013.
[23] Y. Seki and K. Ono, Discriminating Practical Recipes Based on Content Characteristics in Popular Social Recipes. Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, 2014.
[24] Y. Shidochi, T. Takahashi, I. Ide, and H. Murase, Finding Replaceable Materials in Cooking Recipe Texts Considering Characteristic Cooking Actions. Proceedings of the ACM Multimedia Workshop on Multimedia for Cooking and Eating Activities, 2009.
[25] M. Shokouhi and K. Radinsky, Time-Sensitive Query Auto-Completion. Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2012.
[26] T. H. Silva, P. O. De Melo, J. Almeida, M. Musolesi, and A. Loureiro, You are What You Eat (and Drink): Identifying Cultural Boundaries by Analyzing Food & Drink Habits in Foursquare. Proceedings of 8th AAAI International Conference on Weblogs and Social Media, 2014.
[27] D. M. Tax, One-Class Classification. Ph.D Thesis, Delft University of Technology, 2001.
[28] C. Y. Teng, Y. R. Lin, and L. A. Adamic, Recipe Recommendation using Ingredient Networks. Proceedings of the 4th Annual ACM Web Science Conference, 2012.
[29] W. G. Teng, M. J. Hsieh, and M. S. Chen, A Statistical Framework for Mining Substitution Rules. Knowledge and Information Systems, 7(2), 2005.
[30] K. Walter, M. Minor, and R. Bergmann, Workflow Extraction from Cooking Recipes. Proceedings of the International Conference on Case-Based Reasoning Workshops, 2011.
[31] R. West, R. W. White, and E. Horvitz, From Cookies to Cooks: Insights on Dietary Patterns via Analysis of Web Usage Logs. Proceedings of the 22nd International Conference on World Wide Web, 2013.
[32] S. Whiting and J. M. Jose, Recent and Robust Query Auto-Completion. Proceedings of the 23rd International Conference on World Wide Web, 2014.
[33] M. Wiegand, B. Roth, and D. Klakow, Knowledge Acquisition with Natural Language Processing in the Food Domain: Potential and Challenges. Proceedings of the ECAI-Workshop on Cooking with Computers, 2012.
[34] S. Yokoi, K. Doman, T. Hirayama, I. Ide, D. Deguchi, and H. Murase, Typicality Analysis of the Combination of Ingredients in a Cooking Recipe for Assisting the Arrangement of Ingredients. IEEE International Conference on Multimedia & Expo Workshops, 2015.
[35] 呂耀茹, 《由食譜資料探勘料理特徵樣式》. 國立政治大學資訊科學系碩士論文, 2016.		zh_TW