Publications-Theses
Article View/Open
Publication Export
Google ScholarTM
NCCU Library
Citation Infomation
Related Publications in TAIR
| Title | 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 |
| Creator | 朱為丞 |
| Contributor | 許志堅<br>廖峻鋒 朱為丞 |
| Key Words | 體驗行銷 決策樹 新聞推薦 |
| Date | 2016 |
| Date Issued | 1-Mar-2017 17:35:02 (UTC+8) |
| Summary | 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) |
| Description | 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 |
| Type | thesis |
| dc.contributor.advisor | 許志堅<br>廖峻鋒 | zh_TW |
| dc.contributor.author (Authors) | 朱為丞 | zh_TW |
| dc.creator (作者) | 朱為丞 | zh_TW |
| dc.date (日期) | 2016 | en_US |
| dc.date.accessioned | 1-Mar-2017 17:35:02 (UTC+8) | - |
| dc.date.available | 1-Mar-2017 17:35:02 (UTC+8) | - |
| dc.date.issued (上傳時間) | 1-Mar-2017 17:35:02 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0101462013 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/106990 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 數位內容碩士學位學程 | zh_TW |
| dc.description (描述) | 101462013 | zh_TW |
| dc.description.abstract (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。 | zh_TW |
| dc.description.tableofcontents | 第一章 研究動機與目的 8 第一節 研究背景與動機 8 第二節 研究目的 10 第二章 文獻回顧 14 第一節 體驗行銷 14 一、 體驗的定義 14 二、 策略體驗模組 15 三、 體驗媒介 17 第二節 資料探勘(Data Mining) 18 一、 資料探勘的操作步驟 19 二、 資料探勘的技術與方法 19 三、 決策樹演算法(Decision Tree) 22 四、 ID3演算法(Iterative Dichotomiser 3) 22 第三節 李克特量表 25 第四節 軟性新聞 26 第三章 研究設計 32 第一節 使用者行為資料蒐集 35 一、 閱聽人體驗喜好之評估 36 二、 設計問卷 37 三、 問卷測試、修正及問卷調查 42 第二節 機器學習 43 一、 訓練階段 43 二、 執行階段 50 第四章 實驗結果與分析 55 第一節 問卷設計及結果分析 55 一、 問卷設計 55 二、 問卷測試及修正 56 三、 問卷信度分析 57 四、 問卷效度分析 59 第二節 推薦系統衡量指標及驗證方法 60 第三節 實驗結果分析 62 一、 演算法之參數調整 63 二、 實驗結果分析 73 第五章 結論與未來展望 78 | zh_TW |
| dc.format.extent | 3786154 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0101462013 | en_US |
| dc.subject (關鍵詞) | 體驗行銷 | zh_TW |
| dc.subject (關鍵詞) | 決策樹 | zh_TW |
| dc.subject (關鍵詞) | 新聞推薦 | zh_TW |
| dc.title (題名) | 一個考慮閱聽人體驗喜好的電子新聞推薦模型 | zh_TW |
| dc.title (題名) | An e-news recommendation model based on consumer`s experience and preference | en_US |
| dc.type (資料類型) | thesis | en_US |
| dc.relation.reference (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) | zh_TW |