臺灣博碩士論文加值系統

網路的方便性，讓顧客於線上購物之轉換成本較低，而企業要如何利用有限資源，進行顧客流失的預測以及挽留，對企業是相當重要的議題。過往研究中主要以顧客價值進行顧客流失預測，而現今的線上購物平台，大多提供了能讓顧客撰寫評論的平台以及社交的空間，因此過去的顧客流失預測方式，隨著資料特性的不同，已顯得較不適用。顧客藉由購買資訊的分享與交流，包括了產品評論、評價等做為購買決策的考量，並影響未來是否持續消費的可能，而能夠做為考量依據的決策，通常受到具有關係的人所影響。購買紀錄以及產品評論也會隨著時間的累積，令企業能夠分析的資料量逐漸增加。因此本研究提出了基於巨量資料架構分析社交行為與主題模型的顧客流失預測模型，考量了顧客在網路上的社交行為，以及顧客所撰寫的評論中，文字訊息隱含的資訊，而透過主題模型的建置，能將顧客常表達的字詞進行主題分類，以此來建置出顧客本身的偏好特徵，並且以Hadoop平台為實驗基礎架構，節省運算較大量的資料量所需花費的時間。而研究結果也表明，本研究所提出之顧客流失預測模型，與顧客價值進行流失預測的方法比較，擁有較佳的預測結果，並且在Hadoop平台上的執行效率，也明顯高於一般電腦上的執行效率。

Because the convenience of the network, so that customers in the online shopping conversion costs are lower. For enterprises, how to use limited resources to prediction of customer churn and customer retention, is a very important issue. In the past research, customer churn was mainly predicted by customer value. Today''s online shopping platform provides a platform for customers to write reviews and social. Therefore, the past customer churn prediction method, because the different data characteristics, has gradually become not applicable. Customers share and exchange information about purchase, including product reviews, ratings as a consideration for purchasing decisions and influence the possibility of continued consumption in the future. Such decisions may be influenced by the opinions of their friends who have a relationship.
Purchase records and product reviews will be with the accumulation of time, so that enterprises can analyze the amount of data gradually increased. Therefore, this project proposes a prediction of customer churn model based on social behavior analysis and topic model with big data. It considers the social behavior of customers on the Internet and the information implied in the reviews written by the customers. And through the topic model of the building, customers often express the words can be topic classified, and in order to build the customer’s own preferences, and to Hadoop platform for the experimental infrastructure, saving a large amount of data required to calculated the amount of time. The experimental results show that our prediction of customer churn model has better prediction results compared with the other customer churn prediction method, and the execution efficiency on the Hadoop platform is obviously higher than that on the general computer implementation efficiency.

目錄
摘要 I
Abstract II
目錄 III
圖目錄 V
表目錄 VI
第一章 緒論 1
1.1研究背景 1
1.2研究動機與問題 2
1.3研究目的 3
1.4論文架構 4
第二章 文獻探討 5
2.1顧客流失預測 5
2.2 RFM 模型 6
2.3主題模型 7
2.4社交行為與信任關係 12
2.5預測方法 14
第三章 研究方法 16
3.1研究概述 16
3.2顧客價值分析 18
3.3主題模型建置 19
3.3.1 LDA模型參數設定 20
3.3.2 顧客特徵檔建立 21
3.4社交行為與信任關係分析 22
3.4.1社交行為分析 (Social behavior analysis) 22
3.4.2信任關係分析(Trust Relationship Analysis) 25
3.4.3整合社交行為與信任關係 28
3.5顧客流失預測 28
第四章 實驗評估 30
4.1資料收集 30
4.2評估指標 31
4.3 實驗結果與分析 32
4.3.1比較RFM模型分析結果 32
4.3.2主題模型之參數 34
4.3.3社交行為與信任關係對顧客流失的影響 35
4.3.4顧客流失預測結果比較 41
4.4比較Hadoop平台與單一電腦執行效率 43
4.5研究貢獻 44
第五章 結論與未來研究方向 46
5.1結論 46
5.2未來研究方向 46
參考文獻 48

 
圖目錄
圖 1 LDA概念示意圖 9
圖 2 LDA主題模型的生成架構 10
圖 3研究架構圖 16
圖 4社交行為分析之計算說明 24
圖 5信任關係分析之計算說明 27
圖 6 RFM 模型實驗結果 33
圖 7各主題數量之Perplexity計算結果 35
圖 8以社交行為分析值預測顧客流失之Top-N鄰居預測結果 36
圖 9社交行為分值結合顧客相似度之預測結果 37
圖 10以信任關係預測顧客流失之Top-N鄰居預測結果 38
圖 11信任關係結合顧客相似度之預測結果 39
圖 12不同 參數值之預測結果 40

 
表目錄
表 1各組合之顧客流失預測結果 42
表 2執行效率比較表-10棵決策樹 43
表 3執行效率比較表-100棵決策樹 44

[1]E. Bakshy, I. Rosenn, C. Marlow, and L. Adamic, “The role of social networks in information diffusion,” in Proceedings of the 21st international conference on World Wide Web, pp. 519-528, 2012.
[2]D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent Dirichlet Allocation,” Journal of Machine Learning Research, vol. 3, pp. 993-1022, 2003.
[3]C.-H. Cheng, and Y.-S. Chen, “Classifying the segmentation of customer value via RFM model and RS theory,” Expert Systems with Applications, vol. 36, no. 3, Part 1, pp. 4176-4184, 2009.
[4]K. Coussement, and D. Van den Poel, “Integrating the voice of customers through call center emails into a decision support system for churn prediction,” Information & Management, vol. 45, no. 3, pp. 164-174, 2008.
[5]K. Coussement, and D. V. d. Poel, “Improving customer attrition prediction by integrating emotions from client/company interaction emails and evaluating multiple classifiers,” Expert Systems with Applications, vol. 36, no. 3, Part 2, pp. 6127-6134, 2009.
[6]P. Cunningham, and S. J. Delany, “k-Nearest neighbour classifiers,” Multiple Classifier Systems, vol. 34, pp. 1-17, 2007.
[7]K. Dasgupta, R. Singh, B. Viswanathan, D. Chakraborty, S. Mukherjea, A. A. Nanavati, and A. Joshi, “Social ties and their relevance to churn in mobile telecom networks,” in Proceedings of the 11th international conference on Extending database technology: Advances in database technology, pp. 668-677, 2008.
[8]S. T. Dumais, “Latent semantic analysis,” Annual review of information science and technology, vol. 38, no. 1, pp. 188-230, 2004.
[9]P. S. Fader, B. G. Hardie, and K. L. Lee, “RFM and CLV: Using iso-value curves for customer base analysis,” Journal of Marketing Research, vol. 42, no. 4, pp. 415-430, 2005.
[10]H. Fujimoto, M. Etoh, Y. Akinaga, and A. Kinno, “User Profiling based on Latent Topic Modeling,” NTT DOCOMO Technical Journal, vol. 13, no. 3, 2011.
[11]N. Glady, B. Baesens, and C. Croux, “Modeling churn using customer lifetime value,” European Journal of Operational Research, vol. 197, no. 1, pp. 402-411, 2009.
[12]D. Greiner, and T. B. Kinni, 1,001 Ways to Keep Customers Coming Back: WOW Ideas That Make Customers Happy and Will Increase Your Bottom Line, Rocklin, CA: Crown Business, 1999.
[13]T. L. Griffiths, and M. Steyvers, “Finding scientific topics,” Proceedings of the National Academy of Sciences, vol. 101, no. suppl 1, pp. 5228-5235, 2004.
[14]X. Guandong, Z. Yanchun, and Y. Xun, “Modelling User Behaviour for Web Recommendation Using LDA Model,” in IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) pp. 529-532, 2008.
[15]J. Guare, Six degrees of separation: A play: Vintage, 1990.
[16]A. Gustafsson, M. D. Johnson, and I. Roos, “The Effects of Customer Satisfaction, Relationship Commitment Dimensions, and Triggers on Customer Retention,” Journal of Marketing, vol. 69, no. 4, pp. 210-218, 2005.
[17]J. Hadden, A. Tiwari, R. Roy, and D. Ruta, “Computer assisted customer churn management: State-of-the-art and future trends,” Computers & Operations Research, vol. 34, no. 10, pp. 2902-2917, 2007.
[18]M. Hassouna, A. Tarhini, T. Elyas, and M. S. Abou Trab, “Customer Churn in Mobile Markets: A Comparison of Techniques,” International Business Research, vol. 8, no. 6, 2015.
[19]G. Heinrich, “Parameter estimation for text analysis,” Technical report, 2005.
[20]B. Huang, M. T. Kechadi, and B. Buckley, “Customer churn prediction in telecommunications,” Expert Systems with Applications, vol. 39, no. 1, pp. 1414-1425, 2012.
[21]A. M. Hughes, Strategic database marketing: Chicago: Probus Publishing Company, 1994.
[22]C.-S. Hwang, and Y.-P. Chen, “Using trust in collaborative filtering recommendation,” in International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp. 1052-1060, 2007.
[23]H. Hwang, T. Jung, and E. Suh, “An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry,” Expert Systems with Applications, vol. 26, no. 2, pp. 181-188, 2004.
[24]J. Kawale, A. Pal, and J. Srivastava, “Churn Prediction in MMORPGs: A Social Influence Based Approach,” in International Conference on Computational Science and Engineering (CSE) pp. 423-428, 2009.
[25]U. Kaymak, “Fuzzy target selection using RFM variables,” in Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference, pp. 1038-1043 vol.2, 2001.
[26]A. Keramati, R. Jafari-Marandi, M. Aliannejadi, I. Ahmadian, M. Mozaffari, and U. Abbasi, “Improved churn prediction in telecommunication industry using data mining techniques,” Applied Soft Computing, vol. 24, pp. 994-1012, 2014.
[27]D. J. Kim, D. L. Ferrin, and H. R. Rao, “A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents,” Decision Support Systems, vol. 44, no. 2, pp. 544-564, 2008.
[28]B. Luo, S. Peiji, and J. Liu, “Customer Churn Prediction Based on the Decision Tree in Personal Handyphone System Service,” in International Conference on Service Systems and Service Management, pp. 1-5, 2007.
[29]A. McAfee, E. Brynjolfsson, T. H. Davenport, D. Patil, and D. Barton, “Big data,” The management revolution. Harvard Business Review, vol. 90, no. 10, pp. 61-67, 2012.
[30]M. C. Mozer, R. Wolniewicz, D. B. Grimes, E. Johnson, and H. Kaushansky, “Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry,” IEEE Transactions on neural networks, vol. 11, no. 3, pp. 690-696, 2000.
[31]G. Nie, W. Rowe, L. Zhang, Y. Tian, and Y. Shi, “Credit card churn forecasting by logistic regression and decision tree,” Expert Systems with Applications, vol. 38, no. 12, pp. 15273-15285, 2011.
[32]I. Nitzan, and B. Libai, “Social Effects on Customer Retention,” Journal of Marketing, vol. 75, no. 6, pp. 24-38, 2011.
[33]S. Patnaik, M. R. Ghazi, and D. Gangodkar, “Hadoop, MapReduce and HDFS: A Developers Perspective,” Procedia Computer Science, vol. 48, pp. 45-50, 2015.
[34]P. C. Pendharkar, “Genetic algorithm based neural network approaches for predicting churn in cellular wireless network services,” Expert Systems with Applications, vol. 36, no. 3, Part 2, pp. 6714-6720, 2009.
[35]M. Pennacchiotti, and A.-M. Popescu, “A Machine Learning Approach to Twitter User Classification,” in Proceedings of the Fifth International Conference on Weblogs and Social Media (ICWSM), Barcelona, Catalonia, Spain, pp. 281-288, 2011.
[36]P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, “GroupLens: an open architecture for collaborative filtering of netnews,” in Proceedings of the ACM conference on Computer Supported Cooperative Work, pp. 175-186, 1994.
[37]M. Rosen-Zvi, T. Griffiths, M. Steyvers, and P. Smyth, “The author-topic model for authors and documents,” in Proceedings of the 20th conference on Uncertainty in artificial intelligence, Banff, Canada, pp. 487-494, 2004.
[38]L. Sangno, J. Baker, J. Song, and J. C. Wetherbe, “An Empirical Comparison of Four Text Mining Methods,” in 43rd Hawaii International Conference on System Sciences (HICSS), pp. 1-10, 2010.
[39]M. Steyvers, and T. Griffiths, “Probabilistic topic models,” Handbook of latent semantic analysis, vol. 427, no. 7, pp. 424-440, 2007.
[40]A. Tamaddoni Jahromi, S. Stakhovych, and M. Ewing, “Managing B2B customer churn, retention and profitability,” Industrial Marketing Management, vol. 43, no. 7, pp. 1258-1268, 2014.
[41]S. Taneja, C. Gupta, K. Goyal, and D. Gureja, “An enhanced k-nearest neighbor algorithm using information gain and clustering,” in The Fourth International Conference on Advanced Computing & Communication Technologies (ACCT), pp. 325-329, 2014.
[42]C.-F. Tsai, and Y.-H. Lu, “Customer churn prediction by hybrid neural networks,” Expert Systems with Applications, vol. 36, no. 10, pp. 12547-12553, 2009.
[43]C.-F. Tsai, and M.-Y. Chen, “Variable selection by association rules for customer churn prediction of multimedia on demand,” Expert Systems with Applications, vol. 37, no. 3, pp. 2006-2015, 2010.
[44]W. Verbeke, D. Martens, and B. Baesens, “Social network analysis for customer churn prediction,” Applied Soft Computing, vol. 14, Part C, pp. 431-446, 2014.
[45]C.-P. Wei, and I. T. Chiu, “Turning telecommunications call details to churn prediction: a data mining approach,” Expert Systems with Applications, vol. 23, no. 2, pp. 103-112, 2002.
[46]Y. Xie, X. Li, E. W. T. Ngai, and W. Ying, “Customer churn prediction using improved balanced random forests,” Expert Systems with Applications, vol. 36, no. 3, Part 1, pp. 5445-5449, 2009.
[47]B. Yang, Y. Lei, J. Liu, and W. Li, “Social collaborative filtering by trust,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016.
[48]Y. Yang, “Expert network: Effective and efficient learning from human decisions in text categorization and retrieval,” in Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 13-22, 1994.
[49]X. Yu, S. Guo, J. Guo, and X. Huang, “An extended support vector machine forecasting framework for customer churn in e-commerce,” Expert Systems with Applications, vol. 38, no. 3, pp. 1425-1430, 2011.