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研究生: 黃啟軒
Huang, Chi-Hsuan
論文名稱: 即時性促銷活動之公平折扣策略
Fairness-aware Monetary Discount Strategies for Real-Time Promotion Campaign
指導教授: 莊坤達
Chuang, Kun-Ta
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年: 2017
畢業學年度: 105
語文別: 英文
論文頁數: 40
中文關鍵詞: 電子商務即時促銷折扣給予策略
外文關鍵詞: e-commerce, real-time promotion, discount-giving strategies
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  • 在電子商務中,賣家給予商品折扣是一種常見而有效的促銷手段,希望藉此吸引大量的顧客購買產品,然而我們觀察到由於每個人於心中所願意購買一件商品的價格明顯地是不一樣的,現存常見的「固定式折扣策略」其實對於賣家並不是最好的方法,甚至還潛在性地犧牲了賣家的利潤,因此我們於這篇論文中提出了一種全新的即時性促銷系統(Real-Time Promotion,),能在預算給定的折扣促銷活動時間,根據顧客的歷史購買紀錄或行為,即時性地在顧客訪問商品頁面時給予不同的顧客的不同的折扣,藉此追求更高的利潤,除此之外,為了更進步提升即時性促銷系統,我們納入了公平性考量,提出了另一種公平性的即時性促銷系統(Fairness Real-Time Promotion)。
    而為了於該系統中追求最大化利潤,我們在於論文中為即時性促銷系統提出了兩種新的即時折扣給予策略,一種是基於核密度估計(kernel density estimation)與機器學習的折扣策略,另一種是基於湯普森取樣(Thompson Sampling) 的折扣策略,同時也為了公平性的即時性促銷系統提出了公平性的折扣給予策略。
    最後我們藉由了現有的電子商務資料模擬了即時性促銷的實驗,而實驗結果顯示,我們提出的兩種策略皆顯著性地比現存的固定折扣策略能讓賣家獲取更高的利潤,因而證明了我們所提出的即時性促銷系統運用於真實電子商務中的的可行性與前瞻性。

    The effectiveness of monetary promotions has been well reported in the literature to affect shopping decisions for products in real life experience [1]. Nowadays, e-commerce retailers are facing more fierce competition on price promotion in that consumers can easily use a search engine to find another merchant selling an identical product for comparing price.
    We study e-commerce data — shopping receipts collected from email accounts, and conclude that for non-urgent products like books or electronics, buyers are price sensitive and are willing to delay the purchase for better deals. We then present a real-time promotion framework, called the RTP system: a one-time promoted discount price is offered to allure a potential buyer making a decision promptly. Besides, to take it further, we propose another framework, called the FRTP system to take the fairness factor into RTP system.
    To achieve more effectiveness on real-time promotion in pursuit of better profits, we propose two discount-giving strategies for RTP system: an algorithm based on Kernel density estimation, and the other algorithm based on Thompson sampling strategy. And one fairness discount strategy for FRTP system.
    We show that, given a pre-determined discount budget, our algorithms can significantly acquire better revenue in return than classical strategies with simply fixed discount on label price. We then demonstrate its feasibility to be a promising deployment in e-commerce services for real-time promotion.

    中文摘要............... . i Abstract ...............ii Contents ...............iii List of Tables ..............v List of Figures .............. . vi 1 Introduction .............. . 1 2 Problem Formulation ............5 3 Offline Optimal Discount Giving ..........7 4 Online Stochastic Discount Giving .......... . 9 5 Real-Time Promotion Strategy ........... 11 5.1 RTP with Optimal Estimation Strategy ........ . 11 5.2 RTP with Thompson-sampling Strategy ........ 15 6 Fairness-Aware RTP Strategy ........... 18 6.1 Fairness-Aware Discount Giving Strategy ........ 19 6.2 Parameter Optimization ........... 22 7 Performance Evaluation ............ 24 7.1 Experimental Setup ............ 25 7.2 RTP Strategy Performance Analysis ......... 26 7.3 FRTP Strategy Performance Analysis ........29 7.4 Discussions ............. . 31 8 Related Work .............. 34 9 conclusion ..............36

    [1] P. Chandon, B. Wansink, and G. Laurent, “A benefit congruency framework of sales
    promotion effectiveness,” Journal of Marketing, vol. 64, no. 4, pp. 65–81, 2000.
    [2] E. J. McCarthy, Basic Marketing: A Managerial Approach. Homewood, Ill: R.D. Irwin,
    1960.
    [3] I.-H. Hann and C. Terwiesch, “Measuring the frictional costs of online transactions: The
    case of a name-your-own-price channel,” Manage. Sci., vol. 49, no. 11, pp. 1563–1579, Nov.
    2003.
    [4] M. Ding, J. Eliashberg, J. Huber, and R. Saini, “Emotional bidders an analytical and experimental
    examination of consumers’ behavior in a priceline-like reverse auction,” Management
    Science, vol. 51, no. 3, 2005.
    [5] Y. Chen, P. Berkhin, B. Anderson, and N. R. Devanur, “Real-time bidding algorithms
    for performance-based display ad allocation,” in Proceedings of the 17th ACM SIGKDD
    International Conference on Knowledge Discovery and Data Mining, ser. KDD ’11. New
    York, NY, USA: ACM, 2011, pp. 1307–1315.
    [6] S. C. Geyik, S. Faleev, J. Shen, S. O’Donnell, and S. Kolay, “Joint optimization of multiple
    performance metrics in online video advertising,” in Proceedings of the 22Nd ACM
    SIGKDD International Conference on Knowledge Discovery and Data Mining, ser. KDD
    ’16. New York, NY, USA: ACM, 2016, pp. 471–480.
    [7] C.-C. Lin, K.-T. Chuang, W. C.-H.Wu, and M.-S. Chen, “Combining powers of two predictors
    in optimizing real-time bidding strategy under constrained budget,” in Proceedings of
    the 25th ACM International on Conference on Information and Knowledge Management,
    ser. CIKM ’16. New York, NY, USA: ACM, 2016, pp. 2143–2148.
    [8] J. Wang and S. Yuan, “Real-time bidding: A new frontier of computational advertising
    research,” in Proceedings of the Eighth ACM International Conference on Web Search and
    Data Mining, ser. WSDM ’15. New York, NY, USA: ACM, 2015, pp. 415–416.
    [9] S. Yuan, J. Wang, and X. Zhao, “Real-time bidding for online advertising: Measurement
    and analysis,” in Proceedings of the Seventh International Workshop on Data Mining for
    Online Advertising, ser. ADKDD ’13. New York, NY, USA: ACM, 2013, pp. 3:1–3:8.
    [10] W. Zhang, Y. Rong, J. Wang, T. Zhu, and X. Wang, “Feedback control of real-time display
    advertising,” in Proceedings of the Ninth ACM International Conference on Web Search
    and Data Mining, ser. WSDM ’16. New York, NY, USA: ACM, 2016, pp. 407–416.
    [11] S. Thrun, “Efficient exploration in reinforcement learning.” Pittsburgh, PA, Tech. Rep.
    CMU-CS-92-102, January 1992.
    [12] M. U. Kalwani and C. K. Yim, “Consumer price and promotion expectations: An experimental
    study,” Journal of Marketing Research, vol. 29, no. 1, pp. 90–100, 1992.
    [13] C. F. Mela, S. Gupta, and D. R. Lehmann, “The long-term impact of promotion and
    advertising on consumer brand choice,” Journal of Marketing Research, vol. 34, no. 2, pp.
    248–261, 1997.
    [14] H. R. Varian, Microeconomic Analysis, 3rd ed. New York: Norton, 1992.
    [15] Singles Day: Alibaba breaks record sales total., http://www.bbc.com/news/37946470.
    [16] P. Auer, N. Cesa-Bianchi, and P. Fischer, “Finite-time analysis of the multiarmed bandit
    problem,” Mach. Learn., vol. 47, no. 2-3, pp. 235–256, May 2002.
    [17] M. N. Katehakis and A. F. Veinott, Jr., “The multi-armed bandit problem: Decomposition
    and computation,” Math. Oper. Res., vol. 12, no. 2, pp. 262–268, May 1987.
    [18] T. Lai and H. Robbins, “Asymptotically efficient adaptive allocation rules,” Adv. Appl.
    Math., vol. 6, no. 1, pp. 4–22, Mar. 1985.
    [19] B. W. Silverman, Density Estimation for Statistics and Data Analysis. London: Chapman
    & Hall, 1986.
    [20] C.-C. Chang and C.-J. Lin, “Libsvm: A library for support vector machines,” ACM Trans.
    Intell. Syst. Technol., vol. 2, no. 3, pp. 27:1–27:27, May 2011.
    [21] L. Rokach and O. Maimon, Data Mining With Decision Trees: Theory and Applications,
    2nd ed. River Edge, NJ, USA: World Scientific Publishing Co., Inc., 2014.
    [22] S. Agrawal and N. Goyal, “Analysis of thompson sampling for the multi-armed bandit
    problem,” CoRR, vol. abs/1111.1797, 2011.
    [23] C.-W. Hsu, C.-C. Chang, C.-J. Lin et al., “A practical guide to support vector classification,”
    2003.
    [24] F. Kooti, K. Lerman, L. M. Aiello, M. Grbovic, N. Djuric, and V. Radosavljevic, “Portrait
    of an online shopper: Understanding and predicting consumer behavior,” in Proceedings
    of the Ninth ACM International Conference on Web Search and Data Mining, ser. WSDM
    ’16. New York, NY, USA: ACM, 2016, pp. 205–214.
    [25] K. T. Talluri and G. J. Van Ryzin, The theory and practice of revenue management.
    Springer Science & Business Media, 2006, vol. 68.
    [26] D. F. Otero and R. Akhavan-Tabatabaei, “A stochastic dynamic pricing model for the
    multiclass problems in the airline industry,” European Journal of Operational Research,
    vol. 242, no. 1, pp. 188 – 200, 2015.
    [27] S. Sun, R. Law, M. Schuckert, and L. H. N. Fong, An Investigation of Hotel Room Reservation:
    What Are the Diverse Pricing Strategies Among Competing Hotels? Cham: Springer
    International Publishing, 2015, pp. 723–734.
    [28] R. Schlosser, M. Boissier, A. Schober, and M. Uflacker, “How to survive dynamic pricing
    competition in e-commerce,” in Proceedings of the Poster Track of the 10th ACM Conference
    on Recommender Systems (RecSys 2016), Boston, USA, September 17, 2016., 2016.
    [29] Y. Singer and M. Mittal, “Pricing mechanisms for crowdsourcing markets,” in Proceedings
    of the 22Nd International Conference on World Wide Web, ser. WWW ’13. New York,
    NY, USA: ACM, 2013, pp. 1157–1166.
    [30] A. V. den Boer and B. Zwart, “Dynamic pricing and learning with finite inventories,”
    Operations Research, vol. 63, no. 4, pp. 965–978, 2015.
    [31] G. Gallego and G. van Ryzin, “Optimal dynamic pricing of inventories with stochastic
    demand over finite horizons,” Manage. Sci., vol. 40, no. 8, pp. 999–1020, Aug. 1994.
    [32] W. Zhang, T. Zhou, J.Wang, and J. Xu, “Bid-aware gradient descent for unbiased learning
    with censored data in display advertising,” in Proceedings of the 22Nd ACM SIGKDD
    International Conference on Knowledge Discovery and Data Mining, ser. KDD ’16. New
    York, NY, USA: ACM, 2016, pp. 665–674.
    [33] W. C.-H. Wu, M.-Y. Yeh, and M.-S. Chen, “Predicting winning price in real time bidding
    with censored data,” in Proceedings of the 21th ACM SIGKDD International Conference
    on Knowledge Discovery and Data Mining, ser. KDD ’15. New York, NY, USA: ACM,
    2015, pp. 1305–1314.
    [34] W. Zhang, S. Yuan, and J. Wang, “Optimal real-time bidding for display advertising,” in
    Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery
    and Data Mining, ser. KDD ’14. New York, NY, USA: ACM, 2014, pp. 1077–1086.
    [35] Q. Zhao, Y. Zhang, D. Friedman, and F. Tan, “E-commerce recommendation with personalized
    promotion,” in Proceedings of the 9th ACM Conference on Recommender Systems,
    ser. RecSys ’15. New York, NY, USA: ACM, 2015, pp. 219–226.
    [36] E. L. Lee, J. Lou, W. Chen, Y. Chen, S. Lin, Y. Chiang, and K. Chen, “Fairness-aware
    loan recommendation for microfinance services,” in Proceedings of the 2014 International
    Conference on Social Computing, 2014.
    [37] D. Serbos, S. Qi, N. Mamoulis, E. Pitoura, and P. Tsaparas, “Fairness in package-togroup
    recommendations,” in Proceedings of the 26th International Conference on World
    Wide Web, WWW, 2017.
    [38] C. Zhang and J. A. Shah, “Fairness in multi-agent sequential decision-making,” in Advances
    in Neural Information Processing Systems 27: Annual Conference on Neural Information
    Processing Systems, 2014.
    [39] A. Pla, B. L´opez, and J. Murillo, “Multi-dimensional fairness for auction-based resource
    allocation,” Knowl.-Based Syst., 2015.

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