博碩士論文 etd-0812117-134940 詳細資訊

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姓名 張政叡(Cheng-Jui Chang) 電子郵件信箱 E-mail 資料不公開
畢業系所 資訊管理學系研究所(Information Management)
畢業學位 碩士(Master) 畢業時期 106學年第1學期
論文名稱(中) 基於食材食譜的網路和矩陣分解方法發現料理風格
論文名稱(英) Cuisine Discovery based on Recipe-Ingredient Network and Matrix Factorization
  • etd-0812117-134940.pdf
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    紙本論文:2 年後公開 (2019-09-13 公開)

    電子論文:使用者自訂權限:校內 2 年後、校外 2 年後公開

    論文語文/頁數 英文/43
    統計 本論文已被瀏覽 5577 次,被下載 94 次
    摘要(中) 本研究的主要目的是試圖在食譜、食材和料理的製作方法中找出料理風格。料理風格可以被料理來自的文化、食材和處理的步驟或動作所區分,因此,研究中使用了三種方式分析這些資料,分別是:nsNMF、nsNMF with processing sequence constraint和網路分析。
      在文字探勘的領域當中,nsNMF大部分被使用在主題建模(Topic Modeling),分析文章和字詞可以組成的主題,但在這個研究中它被使用在分析食材與食譜,並且找出可能的潛在料理風格,另外一個料理風格的維度-處理的動作,被加入nsNMF的演算法中,並且提出了nsNMF with processing sequence constraint。
    摘要(英) This research proposes an approach to find the cuisines, the types of dishes, from the recipes, ingredients and methods of producing dishes. We believe that the cuisines can be distinguished by the culture, the ingredients, and the processing action of a dish. Therefore, we applied three methods, the nsNMF, the regularized nsNMF and network analysis to analyze recipe data.
     The nsNMF is mostly employed in the field of text mining and implemented the topic modeling, but we used it on the cuisine modeling throw the correlations between recipes and ingredients. On the other hand, another dimension of the cuisines− processing action, was introduced into the modeling to produce the nsNMF with constraint.
     The network analysis was implemented to process the relationships among ingredients. We employed an algorithm, which is greedy−community in network analysis, to detect how many clusters there was in the ingredients. Finally, we analogized what the difference are between the results of the matrix factorization and the network analysis.
  • 矩陣分解
  • 分群
  • 推薦
  • 網路
  • 文字探勘
  • 關鍵字(英)
  • clustering
  • network
  • recommendation
  • matrix factorization
  • text mining
  • 論文目次 論文審定書 i
    誌謝 ii
    摘要 iii
    Abstract iv
    Contents v
    Tables vii
    Figures viii
    1 Introduction 1
    2 Background and Related Works 3
    3 Methodology 7
    3.1 The CoreNLP 7
    3.2 The TF−IDF 8
    3.3 The NMF 9
    3.4 The nsNMF 10
    3.5 The Longest Common Subsequence (LCS) 11
    3.6 The nsNMF with Action Similarity Constraint 12
    3.7 The nsNMF with Processing Sequence Constraint 13
    4 Experimental Result 15
    4.1 Raw Data 15
    4.2 The Recipe−Ingredient Matrix 15
    4.3 TF−IDF 16
    4.4 Mutual Information 17
    4.5 Cuisine Modeling 18
    4.6 The nsNMF with Action Similarity Constraint 19
    4.7 The nsNMF with Processing Sequence Constraint 21
    4.8 Recipe Recommendation 25
    4.9 Network Analysis 26
    5 Conclusion 29
    6 Reference 31
    參考文獻 Ahn, Y.-Y., Ahnert, S. E., Bagrow, J. P., & Barabási, A.-L. (2011). Flavor network and the principles of food pairing. Scientific Reports, 1. Retrieved from http://www.nature.com/srep/2011/111215/srep00196/full/srep00196.html?message-global=remove&WT.i_dcsvid=6042130-NzQwMTE2NDA3OQS2&WT.ec_id=MARKETING&WT.mc_id=SR1205CEPHYS
    Butts, C. T. (2009). Revisiting the Foundations of Network Analysis. Science, 325(5939), 414–416. https://doi.org/10.1126/science.1171022
    Cai, D., He, X., Han, J., & Huang, T. S. (2011). Graph Regularized Nonnegative Matrix Factorization for Data Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(8), 1548–1560. https://doi.org/10.1109/TPAMI.2010.231
    Greene, D., O’Callaghan, D., & Cunningham, P. (2014). How many topics? stability analysis for topic models. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 498–513). Springer. Retrieved from http://link.springer.com/chapter/10.1007/978-3-662-44848-9_32
    Isaacson, D. L., & Madsen, R. W. (1976). Markov chains, theory and applications (Vol. 4). Wiley New York. Retrieved from http://tocs.ulb.tu-darmstadt.de/129935549.pdf
    Kim, H., & Park, H. (2008). Nonnegative Matrix Factorization Based on Alternating Nonnegativity Constrained Least Squares and Active Set Method. SIAM Journal on Matrix Analysis and Applications, 30(2), 713–730. https://doi.org/10.1137/07069239X
    Kolaczyk, E. D., & Csárdi, G. (2014). Statistical analysis of network data with R (Vol. 65). Springer. Retrieved from http://link.springer.com/content/pdf/10.1007/978-1-4939-0983-4.pdf
    Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix Factorization Techniques for Recommender Systems. Computer, 42(8), 30–37. https://doi.org/10.1109/MC.2009.263
    Langville, A. N., & Meyer, C. D. (2011). Google’s PageRank and Beyond: The Science of Search Engine Rankings. Princeton University Press.
    Lehrer, A. (1969). Semantic cuisine. Journal of Linguistics, 5(1), 39–55. https://doi.org/10.1017/S0022226700002048
    Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J. R., Bethard, S., & McClosky, D. (2014). The stanford corenlp natural language processing toolkit. In ACL (System Demonstrations) (pp. 55–60). Retrieved from http://www.aclweb.org/website/old_anthology/P/P14/P14-5.pdf#page=67
    Newman, M. (2010). Networks: an introduction. Oxford university press. Retrieved from https://www.google.com/books?hl=zh-TW&lr=&id=-DgTDAAAQBAJ&oi=fnd&pg=PR5&dq=networks+an+introduction&ots=PBXZgtnUFQ&sig=uXoIH4xUVMZtk8ZYkc8qhaEmxQE
    Pascual-Montano, A., Carazo, J. M., Kochi, K., Lehmann, D., & Pascual-Marqui, R. D. (2006). Nonsmooth nonnegative matrix factorization (nsNMF). IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(3), 403–415. https://doi.org/10.1109/TPAMI.2006.60
    Paterson, M., & Dančík, V. (1994). Longest common subsequences. In Mathematical Foundations of Computer Science 1994 (pp. 127–142). Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58338-6_63
    Strang, G., Strang, G., Strang, G., & Strang, G. (1993). Introduction to linear algebra (Vol. 3). Wellesley-Cambridge Press Wellesley, MA. Retrieved from http://www.ise.ufl.edu/wp-content/uploads/2011/08/ESI4327c_Spring2017_Syllabus-1-30-17.pdf
    Teng, C.-Y., Lin, Y.-R., & Adamic, L. A. (2012). Recipe recommendation using ingredient networks. In Proceedings of the 4th Annual ACM Web Science Conference (pp. 298–307). ACM. Retrieved from http://dl.acm.org/citation.cfm?id=2380757
    Xu, W., Liu, X., & Gong, Y. (2003). Document clustering based on non-negative matrix factorization. In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval (pp. 267–273). ACM. Retrieved from http://dl.acm.org/citation.cfm?id=860485
  • 李珮如 - 召集委員
  • 林耕霈 - 委員
  • 康藝晃 - 指導教授
  • 口試日期 2017-09-05 繳交日期 2017-09-13

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