臺灣博碩士論文加值系統

在虛實整合(O2O)與全通路零售(Omni-Channel Retailing)的環境中，消費者能自由的在線上商店與線下實體店面間穿梭。為因應消費者購物行為轉向行動商務的改變，企業紛紛推出能夠與消費者進行面對面互動的創新物聯網系統。IoT數位看板(Digital Signage)富含多媒體效果，是近年來許多零售企業爭相開發能與消費者互動的廣告行銷工具。例如，導航、提供地區資訊服務、個人化資訊與廣告推播。
對許多大型購物商場或百貨公司而言，數位看板是商業 4.0智慧零售重要的工具。但是目前數位看板系統大都尚未具備感知與互動功能。而近年來，少部分看板雖具備此功能的數位看板系統也有許多不足之處、需要改進之處，例如對於過去沒有交易記錄或匿名消費者沒有好的廣告推播模式以及推播系統沒考慮到人的偏好會因不同環境而有所改變等。
目前學界與業界對物聯網互動數位看板推播系統之研究相對稀少。因此本計畫試圖切入此一物聯網與智慧零售之重要的新研究領域並提出創新解決方案。本計畫主要利用行動裝置應用系統之歷史互動交易資料，並透過資料探勘、推薦系統開發與實驗等研究方法來開發數位看板情境感知推薦分析模型，並將該分析模型運用在數位看板上來增加數位看板廣告投放精準度以提高在全通路零售環境下物聯網互動數位看板的效益。

In the online-to-offline (O2O) and omni-channel retailing environment, consumers can switch different channels very easily. In response to the changing of consumer shopping behavior toward M-Commerce, companies strive to develop novel customer interactive IoT systems to engage consumers. IoT-based interactive digital signage has rich multimedia effects, making it a successful marketing tool for many retail firms; they can use the digital signage to provide wayfinding and location information and personal message and advertising to people who stay in front of the digital signage.
Digital signage has become an important Business 4.0 smart retailing tool for most department stores and large shopping centers. However, most digital signage systems today lack interactive functions to the customer. And few digital signage systems available in the recent years are also insufficient in many aspects, such as, their recommendation model is deficiency in engaging anonymous passengers/visitors or customers without purchasing record and also not considering people will switch the preferences when the condition change.
Few research works concerning the interactive digital signage can be found. Thus, this proposal delves into this new important research area in IoT and smart retailing. This proposal plan to collect historical data set of market transaction collected from mobile device users. Then, based on these data, various research methods from data mining, recommendation system development and experiment are applied to develop an analytical model for context-Aware recommender system for Interactive Digital Signage. This model is applied to a Recommendation analyze to target anonymous viewer and improve system prediction accuracy, in order to enhance the benefits of deploying DS in omni-channel retailing environment.

表 次 V
圖 次 VI
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究方法 2
1.4 研究內容與範圍 2
1.5 研究流程與步驟 3
第二章 文獻回顧 6
2.1 物聯網智慧型互動式數位看板 6
2.2 虛實整合 8
2.3 全通路零售 9
2.4行動商務 9
2.5 資料探勘與關聯規則 10
2.6 推薦系統 13
2.6.1 內容式推薦系統 14
2.6.2 協同過濾系統 17
2.6.3混合式推薦系統 22
第三章 數位看板推薦模型建構 25
3.1系統架構 25
3.2資料庫蒐集 27
3.3資料庫前處理 27
3.4分析模型 28
3.4.1產品分類 28
3.4.2廣告分類 29
3.4.3情境特徵分群 31
3.4.4推薦分析 32
3.4.5 推薦演算法一: 情境類別計數 32
3.4.6 推薦演算法二:廣告分群相似度 34
3.4.7推播清單 36
3.4.8廣告類別與廣告 37
第四章 系統範例與分析 39
4.1資料說明與模擬 39
4.1.1資料說明 39
4.1.1.1產品與廣告資料庫 40
4.1.1.2交易資料庫與會員資料庫 41
4.1.2實驗情境設定 43
4.2推播機制運算 44
4.2.1資料前處理 44
4.2.1資料轉換 44
4.2.2產品與廣告分類 46
4.2.3抓取情境特徵 46
4.2.4推播演算法計算 46
4.3分析與評估 47
4.3.1隨機模型評估 48
4.3.2情境類別計數模型評估 50
4.3.3廣告分群相似度模型評估 52
4.3.4綜合評估 54
4.3.5敏感度分析 56
第五章 結論與建議 60
5.1研究限制 60
5.2結論 60
5.3未來研究方向與建議 61
參考文獻 62

Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering, 17(6), 734-749.
Adomavicius, G., & Tuzhilin, A. (2011). Context-aware recommender systems Recommender systems handbook (pp. 217-253): Springer.
Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. Paper presented at the Data Engineering, 1995. Proceedings of the Eleventh International Conference on.
Ansari, A., Essegaier, S., & Kohli, R. (2000). Internet recommendation systems. Journal of Marketing research, 37(3), 363-375.
Arazy, O., Elsane, I., Shapira, B., & Kumar, N. (2007). Social relationships in recommender systems. Paper presented at the Proc. of the 17th Workshop on Information Technologies & Systems.
Balabanović, M., & Shoham, Y. (1997). Fab: content-based, collaborative recommendation. Communications of the ACM, 40(3), 66-72.
Basu, C., Hirsh, H., & Cohen, W. (1998). Recommendation as classification: Using social and content-based information in recommendation. Paper presented at the AAAI/IAAI.
Bauer, C., Dohmen, P., & Strauss, C. (2011). Interactive digital signage-An innovative service and its future strategies. Paper presented at the Emerging Intelligent Data and Web Technologies (EIDWT), 2011 International Conference on.
Beck, N., & Rygl, D. (2015). Categorization of multiple channel retailing in Multi-, Cross-, and Omni‐Channel Retailing for retailers and retailing. Journal of Retailing and Consumer Services, 27, 170-178.
Berry, M. J., & Linoff, G. (1997). Data mining techniques: for marketing, sales, and customer support: John Wiley & Sons, Inc.
Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109-132.
Bouneffouf, D. (2013). Mobile recommender systems methods: An overview. arXiv preprint arXiv:1305.1745.
Breese, J. S., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. Paper presented at the Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence.
Brin, S., Motwani, R., Ullman, J. D., & Tsur, S. (1997). Dynamic itemset counting and implication rules for market basket data. Paper presented at the ACM SIGMOD Record.
Chen, M.-S., Han, J., & Yu, P. S. (1996). Data mining: an overview from a database perspective. Knowledge and data Engineering, IEEE Transactions on, 8(6), 866-883.
Cuppens, F., Cuppens-Boulahia, N., & Coma, C. (2006). O2O: Virtual private organizations to manage security policy interoperability Information Systems Security (pp. 101-115): Springer.
Demiriz, A. (2004). Enhancing product recommender systems on sparse binary data. Data Mining and Knowledge Discovery, 9(2), 147-170.
Dennis, C., Michon, R., Brakus, J. J., Newman, A., & Alamanos, E. (2012). New insights into the impact of digital signage as a retail atmospheric tool. Journal of Consumer Behaviour, 11(6), 454-466.
Fawcett, T. (2006). An introduction to ROC analysis. Pattern recognition letters, 27(8), 861-874.
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM, 39(11), 27-34.
Forsati, R., Meybodi, M. R., & Rahbar, A. (2009). An efficient algorithm for web recommendation systems. Paper presented at the Computer Systems and Applications, 2009. AICCSA 2009. IEEE/ACS International Conference on.
Gong, S. (2010). A collaborative filtering recommendation algorithm based on user clustering and item clustering. Journal of Software, 5(7), 745-752.
Good, N., Schafer, J. B., Konstan, J. A., Borchers, A., Sarwar, B., Herlocker, J., & Riedl, J. (1999). Combining collaborative filtering with personal agents for better recommendations. Paper presented at the AAAI/IAAI.
Greiner, R., Su, X., Shen, B., & Zhou, W. (2005). Structural extension to logistic regression: Discriminative parameter learning of belief net classifiers. Machine learning, 59(3), 297-322.
Guan, L., & Lu, H. (2012). Recommend items for user in social networking services with CF. Paper presented at the Computer Science & Service System (CSSS), 2012 International Conference on.
Huang, C.-B., & Gong, S.-J. (2008). Employing rough set theory to alleviate the sparsity issue in recommender system. Paper presented at the Machine Learning and Cybernetics, 2008 International Conference on.
Hyun, W., Huh, M.-Y., Kim, S., & Kang, S. (2012). Considerations on Audience Measurement Procedures for Digital Signage Service. International Journal of Control and Automation, SERSC, 5(2), 123-130.
Kalakota, R., & Robinson, M. (2001). eBusiness 2.0: Roadmap for success. MA: Addison-Wiley Inc.
Kim, H.-N., Ji, A.-T., Ha, I., & Jo, G.-S. (2010). Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation. Electronic Commerce Research and Applications, 9(1), 73-83.
Kitts, B., Freed, D., & Vrieze, M. (2000). Cross-sell: a fast promotion-tunable customer-item recommendation method based on conditionally independent probabilities. Paper presented at the Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining.
Lekakos, G., & Giaglis, G. M. (2006). Improving the prediction accuracy of recommendation algorithms: Approaches anchored on human factors. Interacting with computers, 18(3), 410-431.
Lemire, D., & Maclachlan, A. (2005). Slope One Predictors for Online Rating-Based Collaborative Filtering. Paper presented at the SDM.
Liang, M., Yang, X., & Ou, H. (2014). The Measurement of the Consumer Trust to O2O E-Commerce Based on Fuzzy Evaluation. Paper presented at the Computational Sciences and Optimization (CSO), 2014 Seventh International Joint Conference on.
Müller-Veerse, F. (1999). Mobile commerce report. Durlacher Research Ltd.
Müller, J., & Krüger, A. (2007). Competing for your attention: negative externalities in digital signage advertising. Paper presented at the Workshop at Pervasive 2007 Designing and Evaluating Ambient Information Systems.
McKelvey, N., Curran, K., & Subaginy, N. (2015). The Internet of Things.
Mishra, R., Kumar, P., & Bhasker, B. (2015). A web recommendation system considering sequential information. Decision Support Systems, 75, 1-10.
Niwattanakul, S., Singthongchai, J., Naenudorn, E., & Wanapu, S. (2013). Using of Jaccard coefficient for keywords similarity. Paper presented at the Proceedings of the International MultiConference of Engineers and Computer Scientists.
Oh, J.-M., & Moon, N. (2012). User-selectable interactive recommendation system in mobile environment. Multimedia Tools and Applications, 57(2), 295-313.
Pan, R., Zhou, Y., Cao, B., Liu, N. N., Lukose, R., Scholz, M., & Yang, Q. (2008). One-class collaborative filtering. Paper presented at the 2008 Eighth IEEE International Conference on Data Mining.
Pazzani, M. J. (1999). A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, 13(5-6), 393-408.
Pazzani, M. J., & Billsus, D. (2007). Content-based recommendation systems The adaptive web (pp. 325-341): Springer.
Phang, C. W., Tan, C.-H., Sutanto, J., Magagna, F., & Lu, X. (2014). Leveraging O2O commerce for product promotion: an empirical investigation in Mainland China. IEEE Transactions on Engineering Management, 61(4), 623-632.
Rampell, A. (2010). Why Online2Offline Commerce is a trillion dollar opportunity. techcrunch. com,(available online at http://techcrunch. com/2010/08/07/why-online2offline-commerce-is-a-trillion-dollaropportunity/).
Ravnik, R., & Solina, F. (2013). Audience measurement of digital signage: Quantitative study in real-world environment using computer vision. Interacting with computers, 25(3), 218-228.
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2000). Analysis of recommendation algorithms for e-commerce. Paper presented at the Proceedings of the 2nd ACM conference on Electronic commerce.
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. Paper presented at the Proceedings of the 10th international conference on World Wide Web.
Satoh, I. (2010). An agent-based framework for context-aware digital signage. Paper presented at the Ambient Intelligence and Future Trends-International Symposium on Ambient Intelligence (ISAmI 2010).
Schaeffler, J. (2008). Digital Signage: Software. Networks, Advertising, and Displays, Focal, USA.
Schafer, J. B., Frankowski, D., Herlocker, J., & Sen, S. (2007). Collaborative filtering recommender systems The adaptive web (pp. 291-324): Springer.
Schein, A. I., Popescul, A., Ungar, L. H., & Pennock, D. M. (2002). Methods and metrics for cold-start recommendations. Paper presented at the Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval.
Scherer, S., Glodek, M., Schwenker, F., Campbell, N., & Palm, G. (2012). Spotting laughter in natural multiparty conversations: A comparison of automatic online and offline approaches using audiovisual data. ACM Transactions on Interactive Intelligent Systems (TiiS), 2(1), 4.
Shardanand, U., & Maes, P. (1995). Social information filtering: algorithms for automating “word of mouth”. Paper presented at the Proceedings of the SIGCHI conference on Human factors in computing systems.
Su, X., & Khoshgoftaar, T. M. (2006). Collaborative filtering for multi-class data using belief nets algorithms. Paper presented at the Tools with Artificial Intelligence, 2006. ICTAI'06. 18th IEEE International Conference on.
Su, X., & Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques. Advances in artificial intelligence, 2009, 4.
Tian, P., Sanjay, A. V., Chiranjeevi, K., & Malik, S. M. (2012). Intelligent advertising framework for digital signage. Paper presented at the Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining.
Tian, P., Sanjay, A. V., Malik, S., Chiranjeevi, K., Phadnis, S., & Patel, U. (2012). Realizing Targeted Advertising in Digital Signage with AVA and Data Mining. Paper presented at the Proceedings of International MultiConference on Engineers and Computer Scientists.
Turban, E., King, D., Lee, J. K., Liang, T.-P., & Turban, D. C. (2015). Electronic commerce: A managerial and social networks perspective: Springer.
ULUCAN, S. (2005). A recommendation system combining context-awareness and user profiling in mobile environment. Citeseer.
Verhoef, P. C., Kannan, P., & Inman, J. J. (2015). From multi-channel retailing to omni-channel retailing: introduction to the special issue on multi-channel retailing. Journal of Retailing, 91(2), 174-181.
Wang, H.-F., & Wu, C.-T. (2009). A mathematical model for product selection strategies in a recommender system. Expert Systems with Applications, 36(3), 7299-7308.
Wang, H.-F., & Wu, C.-T. (2012). A strategy-oriented operation module for recommender systems in E-commerce. Computers & Operations Research, 39(8), 1837-1849.
Wang, J., De Vries, A. P., & Reinders, M. J. (2006). Unifying user-based and item-based collaborative filtering approaches by similarity fusion. Paper presented at the Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval.
Yin, K.-c., Wang, H.-c., Yang, D.-l., & Wu, J. (2012). A study on the effectiveness of digital signage advertisement. Paper presented at the Computer, Consumer and Control (IS3C), 2012 International Symposium on.
Zhao, Z., & Balagué, C. (2015). Designing branded mobile apps: Fundamentals and recommendations. Business Horizons, 58(3), 305-315.
游瑞穎. (2013). 多媒體數位看板整合開發環境之設計與實作. (碩士), 國立中山大學, 高雄市.