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    題名: 一個考慮閱聽人體驗喜好的電子新聞推薦模型
    An e-news recommendation model based on consumer`s experience and preference
    作者: 朱為丞
    貢獻者: 許志堅
    廖峻鋒
    朱為丞
    關鍵詞: 體驗行銷
    決策樹
    新聞推薦
    日期: 2016
    上傳時間: 2017-03-01 17:35:02 (UTC+8)
    摘要: 本研究嘗試建立一個考慮使用者體驗喜好之電子新聞推薦模型。我們以Schmitt提出之策略體驗模組為基礎了解使用者對各體驗之重視程度，分析使用者對各種不同型式體驗之重視程度以作為ID3決策樹機器學習演算法的輸入屬性，並以消費者對於電子新聞的喜好與否作為目標屬性，利用決策樹演算法計算這些輸入屬性(使用者對各種不同型式體驗之喜好)與目標屬性(使用者對於電子新聞的選擇)之間的關聯式規則。接著利用這些規則來建構一個預測模型，以評估閱聽人對於未知電子新聞的接受程度，從而建立一個能有效符合使用者個人體驗喜好之新聞推薦模型。
    參考文獻: [1] M. Brandt. 2014 ,Apr 23.Mobile is the New Media Star [Online]. Available:http://www.statista.com/chart/2168/share-of-time-spent-per-day-with-major-media/
    [2] Internet Overtakes Newspaper As Newsoutlet [Online]. Available:http://www.people-press.org/2008/12/23/internet-overtakes-newspapers-as-news-outlet/
    [3] 173 Million Adults Engaged with Newspaper Digital Content in January [Online]. Available:http://mediamanagersclub.org/173-million-adults-engaged-newspaper-digital-content-january-naa
    [4] Resnick, P., N. Iakovou, M. Sushak, P. Bergstrom, and J. Riedl. GroupLens: An open architecture for collaborative filtering of netnews. In Proceedings of the 1994 Computer Supported Cooperative Work Conference, 1994.
    [5] Kompan, M., Bieliková, M., 2010. Content-Based News Recommendation. In Proc. of the 11th Conf. EC-WEB, Springer, 61-72.
    [6] F. Frasincar, J. Borsje, and L. Levering. A Semantic Web-Based Approach for Building Personalized News Services. International Journal of E-Business Research, 5(3):35–53, 2009.
    [7] IJntema W, Goossen F, Frasincar F, Hogenboom F. Ontology-based news recommendation. In: Proc 2010 EDBT/ICDT Workshops, Lausanne, Switzerland; 2010. pp 1–6.
    [8] Konstan, J. A., B. N. Miller, D. Maltz, J. L. Herlocker, L. R. Gordon, and J. Riedl. GroupLens: Applying collaborative filtering to Usenet news. Communications of the ACM, 40(3):77-87, 1997.
    [9] A. Das, M. Datar, A. Garg, and S. Rajaram. Google news personalization: scalable online collaborative filtering. In Proc. of the 16th International World Wide Web Conf., 2007.
    [10] Liu, J., Dolan, P., Pedersen, E.R.: Personalized news recommendation based on click behavior. In Rich et al., eds.: Proc. of 14th Int. Conf. on Intelligent User Interfaces (IUI), ACM (2010) 31–40
    [11] Pine, B.J., and Gilmore, J.H. The Experience Economy. Boston: Harvard Business School Press, 2011.
    [12] Alex Simonson, Bernd H. Schmitt. Marketing Aesthetics: The Strategic Management of Brands, Identity and Image[M]. Free Press, August 30, 1997
    [13] Schmitt, B.H. (1999). Experiential Marketing: How to Get Customers to Sense, Feel, Think, Act, and Relate to Your Company and Brands. New York: Free Press.
    [14] Tan, P.-N., Steinbach, M., and Kumar, V. 2005. Introduction to Data Mining. Addison-Wesley.
    [15] Han, Jiawei, Kamber, Micheline, 2000. Data Mining: Concepts and Techniques.
    Morgan Kaufmann.
    [16] I.H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and
    Techniques with Java Implementations. Morgan Kaufmann, 2011.
    [17] Quinlan, J.R. (1986). Induction of decision trees. Machine Learning, 1, 81-106.
    [18] Clair, D. C. St., Sabharwal, C. L. and Hacke, K. R. ,“Formation of clusters and resolution of ordinal attributes in ID3 classification trees”,Proc. of ACM/SIGAPP Symposium on Applied Computing: Technological Challenges of the 1990’s, pp.590-597, 1992.
    [19] Patterson,T.E.2000. Doing Well and Doing Good:How Soft News and Critical Journalism Are Shrinking the News Audience and Weakening Democracy—And What News Outlets Can Do about It. Cambridge, MA: The Joan Shorenstein Center for Press, Politics, and Public Policy at Harvard University
    [20] Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39,31-36.
    [21] F. Ricci, L. Rokach, B. Shapira, P. B. Kantor , Recommender Systems
    Handbook, Springer, 2011
    [22] Gunawardana, A., Shani, G.: A survey of accuracy evaluation metrics of recommendation tasks. J. Mach. Learn. Res. 10, 2935–2962 (2009)
    描述: 碩士
    國立政治大學
    數位內容碩士學位學程
    101462013
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0101462013
    資料類型: thesis
    顯示於類別:[數位內容碩士學位學程] 學位論文

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