政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/127204

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/127204

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政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 > Item 140.119/127204

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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:	莊皓鈞周彥君 Chuang, Hao-Chun 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:	2019-11-06 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 ti Bonchi, 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
DOI:	10.6814/NCCU201901207
顯示於類別:	[資訊管理學系] 學位論文

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