學術產出-Theses
Article View/Open
Publication Export
-
題名 零售商業分析：購物籃資料的指數隨機圖模型
Retail Business Analytics: Exponential Random Graph Modeling of Market Basket Data作者 張月馨
Chang, Yueh-Hsin貢獻者 莊皓鈞<br>周彥君
Chuang, Hao-Chun<br>Chou, Yen-Chun
張月馨
Chang, Yueh-Hsin關鍵詞 零售業
指數隨機圖模型
二元依賴模型
商品網路圖
Retailing
ERGM
Dyadic Dependence Model
Product Network日期 2019 上傳時間 6-Nov-2019 15:25:01 (UTC+8) 摘要 購物籃分析在當代的零售商業分析扮演重要的角色，可以幫助零售商了解消費者之購物傾向，但是購物籃分析缺乏一般化的規則解釋商品彼此併買的潛在原因，因此本研究採用指數隨機圖模型(Exponential Random Graph Modeling, ERGM)解決購物籃分析對商品連結缺乏解釋性的限制。指數隨機圖模型是用來檢測隨機圖或是網路圖模型中彼此連結關係模式的工具，對欲解釋之網路圖結構特徵提供良好的分析方法。本研究主要探討如何應用超商零售業之交易資料，設計一套以指數隨機圖模型為基礎，加入結構特徵之二元依賴模型(Hunter, Handcock, Butts, Goodreau, & Morris, 2008)之分析應用流程，幫助零售業者對行銷策略提供更好的應用方向。
Nowadays, market basket analysis plays an important role in retail business analysis, as it allows the retailer to develop a better understanding of consumers’ purchasing tendency. However, market basket analysis lacks general rules to explain the potential reasons why the products are bought together. Therefore, this research uses Exponential Random Graph Model (ERGM) to enhance the explanatory power on discovered co-purchase relationships. The ERGM is a technique for assessing interdependencies between nodes in random graphs or networks, and it enables analysts to uncover structural features in networks. With more than three million transaction records of a leading convenience store in Taiwan, our research focuses on how to model these transaction data using ERGM and combines the Dyadic Dependence Model (Hunter, Handcock, Butts, Goodreau, & Morris, 2008) to design a new analysis process. The proposed process is aimed at guiding retailers to develop better marketing strategies regarding bundle selling/co-purchase.參考文獻 Agrawal, R., Imielinski, T., & Swami, A. (1993). Database mining: A performance perspective. IEEE Transactions on Knowledge and Data Engineering, 5(6), 914–925.Agrawal, R., & Srikant, R. (1994). Fast algorithm for mining association rules in large database. Research Report Res that lead: A social network approach to leadership. The Leadership Quarterly, 17, 419-439.J 9839, IBM Almaden Research Center, Santiago, Chile.Akter, S. & Fosso, Wamba, S. (2016). Big Data Analytics in E- Commerce: A Systematic Review and Agenda for Future Research.Balkundi, P., & Kilduff, M. (2006). The tiBonchi, F., Castillo, C., Gionis, A., & Jaimes, A. (2011). Social network analysis and mining for business applications. ACM Transactions on Intelligent Systems and Technology, 2, 3, Article 22.Chen, Y. L., Tang, K., Shen, R. J., & Hu, Y. H. (2005). Market basket analysis in a multiple store environment. Decision Support Systems, 40(2), 339–354.Chiu, C., Ku, Y., Lie, L., & Chen, Y. (2011). Internet auction fraud detection using social network analysis and classification tree approaches. International Journal of Electronic Commerce, 15(3), 123–147.Coleman, J., Menzel, H., & Katz, E. (1966). Medical Innovations: A Diffusion Study. Bobbs Merrill.Erdös, P., & Rényi, A. (1959). On Random Graphs, I. Publicationes Mathematicae(Debrecen), 6, 290-297.Frank, O., Strauss, D. (1986). Markov graphs. Journal of the American Statistical Association, 81(395), 832–842.Freeman, L.C. (1997). A set of measures of centrality based on betweenness. Sociometry, 40, 35–41.Huang, Z., H. Chen, D. Zeng. (2004). Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Transactions on Intelligent Systems and Technology, 22(1), 116–142.Hunter, D. R., Handcock, M. S., Butts, C. T., Goodreau, S. M., & Morris, M. (2008). Ergm: A package to fit, simulate and diagnose exponential-family models for networks. Journal of Statistical Software, 24(3), 1-29.Jin, K. (2013). Social Network Analysis of Facebook Brand Communities. Saint Mary’s University, Halifax, Nova Scotia. Research Project for Degree of Business Administration, Saint Mary’s University.Karonski, M. (1982). A review of random graphs. Journal of Graph Theory, 6(4), 349-389.Kaur, M., Kang, S. (2016). Market Basket Analysis: Identifying the changing trends of market data using association rule mining, International conference on Computational Modeling and Security. Procedia Computer Science, 78-85.Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Guttmann, M., Jebara, T., King, G., Macy, M., Roy, D., & Alstynei, M. V. (2009). Computational social science. Sci, 323, 5915, 721–723.Meng, W., Chaokun, W., Jeffrey, X. Y., Jun, Z. (2015). Community detection in social networks: an in-depth benchmarking study with a procedure-oriented framework. Proceedings of the VLDB Endowment, v.8 n.10, p.998-1009.Mitchell, J. C. (1969). The Concept and Use of Social Networks. Pp. 1-50 in Social Networks in Urban Situations: Analyses of Personal Relationships in Central African Towns, edited by J. Clyde Mitchell. Manchester, England: Manchester University Press.Mostafa, M. (2015). Knowledge discovery of hidden consumer purchase behavior: a market basket analysis IJDATS, 7 (4) (2015), pp. 384-405.Otte, E., & Rousseau, R. (2002). Social network analysis: a powerful strategy, also for the information sciences. J. Information Science, 28, 441-453.Qi, X., Fuller, E., Wu, Q., Wu, Y., & Zhang, C.-Q. (2012). Laplacian centrality: A new centrality measure for weighted networks. Information Science, 194, 240–253.Raeder, T., Chawla, N. V. (2009). Modeling a store’s product space as a social network. In Proceedings of the 2009 international conference on advances in social network analysis and mining. Athens, Greece, pp. 164–169.Rivera, M. T., Soderstrom, S. B., & Uzzi, B. (2010). Dynamics of dyads in social network: Assortative, relational, and proximity mechanisms. Annual Review of Sociology, 36, 91-115.Scott, John. (1991). Social network analysis: A handbook. London: Sage.Snijders, T. A. B. (2011a). Statistical models for social networks. Annual Review of Sociology, 37, 131-153.Tewari, A.S., Kumar, A., & Barman, A.G. (2014). Book recommendation system based on combine features of content based filtering, collaborative filtering and association rule mining. Advance Computing Conference (IACC), IEEE International, 500 - 503.Wasserman, S., & Faust, K. (1994). Social network analysis. Cambridge, MA: Cambridge University Press.Wasserman, S., Pattison, P.E. (1996). Logit models and logistic regression for social networks. I. An introduction to Markov graphs and p*. Psychometrika, 61(3), 401–425.Watts, D. J. (2004). The “new” science of networks. Ann. Rev. Sociol. 30, 243–270. Wellman, B., & Berkowitz, S. D (Eds.). (1988). Social structures: A network approach. Cambridge: Cambridge University Press.Zinoviev D., Zhu Z., Li K. (2015). Building mini-categories in product networks. In Complex Networks VI. Vol. 597. Springer, Cham. 描述 碩士
國立政治大學
資訊管理學系
106356002資料來源 http://thesis.lib.nccu.edu.tw/record/#G0106356002 資料類型 thesis dc.contributor.advisor 莊皓鈞<br>周彥君 zh_TW dc.contributor.advisor Chuang, Hao-Chun<br>Chou, Yen-Chun en_US dc.contributor.author (Authors) 張月馨 zh_TW dc.contributor.author (Authors) Chang, Yueh-Hsin en_US dc.creator (作者) 張月馨 zh_TW dc.creator (作者) Chang, Yueh-Hsin en_US dc.date (日期) 2019 en_US dc.date.accessioned 6-Nov-2019 15:25:01 (UTC+8) - dc.date.available 6-Nov-2019 15:25:01 (UTC+8) - dc.date.issued (上傳時間) 6-Nov-2019 15:25:01 (UTC+8) - dc.identifier (Other Identifiers) G0106356002 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/127204 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊管理學系 zh_TW dc.description (描述) 106356002 zh_TW dc.description.abstract (摘要) 購物籃分析在當代的零售商業分析扮演重要的角色，可以幫助零售商了解消費者之購物傾向，但是購物籃分析缺乏一般化的規則解釋商品彼此併買的潛在原因，因此本研究採用指數隨機圖模型(Exponential Random Graph Modeling, ERGM)解決購物籃分析對商品連結缺乏解釋性的限制。指數隨機圖模型是用來檢測隨機圖或是網路圖模型中彼此連結關係模式的工具，對欲解釋之網路圖結構特徵提供良好的分析方法。本研究主要探討如何應用超商零售業之交易資料，設計一套以指數隨機圖模型為基礎，加入結構特徵之二元依賴模型(Hunter, Handcock, Butts, Goodreau, & Morris, 2008)之分析應用流程，幫助零售業者對行銷策略提供更好的應用方向。 zh_TW dc.description.abstract (摘要) Nowadays, market basket analysis plays an important role in retail business analysis, as it allows the retailer to develop a better understanding of consumers’ purchasing tendency. However, market basket analysis lacks general rules to explain the potential reasons why the products are bought together. Therefore, this research uses Exponential Random Graph Model (ERGM) to enhance the explanatory power on discovered co-purchase relationships. The ERGM is a technique for assessing interdependencies between nodes in random graphs or networks, and it enables analysts to uncover structural features in networks. With more than three million transaction records of a leading convenience store in Taiwan, our research focuses on how to model these transaction data using ERGM and combines the Dyadic Dependence Model (Hunter, Handcock, Butts, Goodreau, & Morris, 2008) to design a new analysis process. The proposed process is aimed at guiding retailers to develop better marketing strategies regarding bundle selling/co-purchase. en_US dc.description.tableofcontents 第一章 緒論 1第二章 文獻回顧 3第一節 關聯規則(Association Rule) 3第二節 社群網路分析(Social Network Analysis, SNA) 6第三章 模型介紹 9第四章 ERGM模型分析結果 12第一節 資料敘述 12第二節 資料分析結果 19第五章 結論 34參考文獻 36 zh_TW dc.format.extent 1839095 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0106356002 en_US dc.subject (關鍵詞) 零售業 zh_TW dc.subject (關鍵詞) 指數隨機圖模型 zh_TW dc.subject (關鍵詞) 二元依賴模型 zh_TW dc.subject (關鍵詞) 商品網路圖 zh_TW dc.subject (關鍵詞) Retailing en_US dc.subject (關鍵詞) ERGM en_US dc.subject (關鍵詞) Dyadic Dependence Model en_US dc.subject (關鍵詞) Product Network en_US dc.title (題名) 零售商業分析：購物籃資料的指數隨機圖模型 zh_TW dc.title (題名) Retail Business Analytics: Exponential Random Graph Modeling of Market Basket Data en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Agrawal, R., Imielinski, T., & Swami, A. (1993). Database mining: A performance perspective. IEEE Transactions on Knowledge and Data Engineering, 5(6), 914–925.Agrawal, R., & Srikant, R. (1994). Fast algorithm for mining association rules in large database. Research Report Res that lead: A social network approach to leadership. The Leadership Quarterly, 17, 419-439.J 9839, IBM Almaden Research Center, Santiago, Chile.Akter, S. & Fosso, Wamba, S. (2016). Big Data Analytics in E- Commerce: A Systematic Review and Agenda for Future Research.Balkundi, P., & Kilduff, M. (2006). The tiBonchi, F., Castillo, C., Gionis, A., & Jaimes, A. (2011). Social network analysis and mining for business applications. ACM Transactions on Intelligent Systems and Technology, 2, 3, Article 22.Chen, Y. L., Tang, K., Shen, R. J., & Hu, Y. H. (2005). Market basket analysis in a multiple store environment. Decision Support Systems, 40(2), 339–354.Chiu, C., Ku, Y., Lie, L., & Chen, Y. (2011). Internet auction fraud detection using social network analysis and classification tree approaches. International Journal of Electronic Commerce, 15(3), 123–147.Coleman, J., Menzel, H., & Katz, E. (1966). Medical Innovations: A Diffusion Study. Bobbs Merrill.Erdös, P., & Rényi, A. (1959). On Random Graphs, I. Publicationes Mathematicae(Debrecen), 6, 290-297.Frank, O., Strauss, D. (1986). Markov graphs. Journal of the American Statistical Association, 81(395), 832–842.Freeman, L.C. (1997). A set of measures of centrality based on betweenness. Sociometry, 40, 35–41.Huang, Z., H. Chen, D. Zeng. (2004). Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Transactions on Intelligent Systems and Technology, 22(1), 116–142.Hunter, D. R., Handcock, M. S., Butts, C. T., Goodreau, S. M., & Morris, M. (2008). Ergm: A package to fit, simulate and diagnose exponential-family models for networks. Journal of Statistical Software, 24(3), 1-29.Jin, K. (2013). Social Network Analysis of Facebook Brand Communities. Saint Mary’s University, Halifax, Nova Scotia. Research Project for Degree of Business Administration, Saint Mary’s University.Karonski, M. (1982). A review of random graphs. Journal of Graph Theory, 6(4), 349-389.Kaur, M., Kang, S. (2016). Market Basket Analysis: Identifying the changing trends of market data using association rule mining, International conference on Computational Modeling and Security. Procedia Computer Science, 78-85.Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Guttmann, M., Jebara, T., King, G., Macy, M., Roy, D., & Alstynei, M. V. (2009). Computational social science. Sci, 323, 5915, 721–723.Meng, W., Chaokun, W., Jeffrey, X. Y., Jun, Z. (2015). Community detection in social networks: an in-depth benchmarking study with a procedure-oriented framework. Proceedings of the VLDB Endowment, v.8 n.10, p.998-1009.Mitchell, J. C. (1969). The Concept and Use of Social Networks. Pp. 1-50 in Social Networks in Urban Situations: Analyses of Personal Relationships in Central African Towns, edited by J. Clyde Mitchell. Manchester, England: Manchester University Press.Mostafa, M. (2015). Knowledge discovery of hidden consumer purchase behavior: a market basket analysis IJDATS, 7 (4) (2015), pp. 384-405.Otte, E., & Rousseau, R. (2002). Social network analysis: a powerful strategy, also for the information sciences. J. Information Science, 28, 441-453.Qi, X., Fuller, E., Wu, Q., Wu, Y., & Zhang, C.-Q. (2012). Laplacian centrality: A new centrality measure for weighted networks. Information Science, 194, 240–253.Raeder, T., Chawla, N. V. (2009). Modeling a store’s product space as a social network. In Proceedings of the 2009 international conference on advances in social network analysis and mining. Athens, Greece, pp. 164–169.Rivera, M. T., Soderstrom, S. B., & Uzzi, B. (2010). Dynamics of dyads in social network: Assortative, relational, and proximity mechanisms. Annual Review of Sociology, 36, 91-115.Scott, John. (1991). Social network analysis: A handbook. London: Sage.Snijders, T. A. B. (2011a). Statistical models for social networks. Annual Review of Sociology, 37, 131-153.Tewari, A.S., Kumar, A., & Barman, A.G. (2014). Book recommendation system based on combine features of content based filtering, collaborative filtering and association rule mining. Advance Computing Conference (IACC), IEEE International, 500 - 503.Wasserman, S., & Faust, K. (1994). Social network analysis. Cambridge, MA: Cambridge University Press.Wasserman, S., Pattison, P.E. (1996). Logit models and logistic regression for social networks. I. An introduction to Markov graphs and p*. Psychometrika, 61(3), 401–425.Watts, D. J. (2004). The “new” science of networks. Ann. Rev. Sociol. 30, 243–270. Wellman, B., & Berkowitz, S. D (Eds.). (1988). Social structures: A network approach. Cambridge: Cambridge University Press.Zinoviev D., Zhu Z., Li K. (2015). Building mini-categories in product networks. In Complex Networks VI. Vol. 597. Springer, Cham. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU201901207 en_US
