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題名 個人化廣告推薦軟體之設計與實做
The Design and implementations of a personalized advertisement recommendation software
作者 張宏嘉
Chang, Hung Chia
貢獻者 沈錳坤
Shan, Man Kwan
張宏嘉
Chang, Hung Chia
關鍵詞 個人化
廣告
推薦系統
Personalized
Advertisement
Recommendar System
日期 2007
上傳時間 9-May-2016 12:02:24 (UTC+8)
摘要 鑑於國人網路依賴度越來越高，每人每日透過網路獲取豐富的資訊及資料，然而由於資訊過多也造成使用者不知道自己真正想要的資訊，因此找尋出個人化資訊勢必成為未來研究的方向。一般網路行銷廣告都是在分析使用者在網站上的行為，然而鮮少去分析到使用者端環境及儲存的資料。相較於使用者在網站上的行為，使用者端所留下的行為資訊更能夠反應出使用者的真正興趣，因此本研究主要探討在個人使用環境(如：PC、NB)中，透過使用者最常接觸的三種途徑(包括：上網瀏覽資訊、信件資訊及常用檔案內容資訊)來獲取使用者興趣的資訊，並且建構出一套個人化廣告推薦系統常駐於使用者端即時(real-time)記錄使用者的行為資訊。本系統運用推薦技術(Recommendation Technique)搭配關聯式法則(Association Rule)來將這些資訊有效的過濾並關聯出使用者的喜好及興趣，同時利用這些資訊上網找尋合適的廣告資料，用以建構出個人化廣告推薦模式。
The rapid growth of Internet has changed the patterns of our life. Everyone can gain rich information on internet, but plenty of information will confuse user`s ability to determine whether these information are useful. Therefore, the further trend is to discover personalized information. Many researches about internet marketing advertisement are mining user`s behavior in website server, but scarce researches focus on client. Compared to user`s behavior in website, the behavior information which stays in local environment (ex. PC, NB) can reflect user`s profile more. Thus, this research mainly discuss how to record user`s behavior information containing Web Page Title, E-mail Subject and Document Content in local environment and how to construct a personalized advertisement recommendation system resident in memory of local environment for timely (On-line) collecting user`s behavior information to create “user profile” using recommendation technique and association rule. This system will utilize “user profile” to provide appropriate personalized advertisement for user. Finally, we apply several experiments to verify the feasibility of our system.
第一章 導論............1
     1.1 研究背景..........1
     1.2 研究動機..........2
     1.3 研究問題與目的....2
     第二章 技術背景與相關研究.........4
     2.1 An Introduction to Recommendation........4
     2.2 Recommendation Approach...........5
     2.2.1 內容導向式（Content-based Filtering）.........5
     2.2.2 協同過濾式 （Collaborative Filtering, CF）....8
     2.2.3 混合式 （Hybrid-based filtering）.........9
     2.3 Extensions for Recommendation Techniques...10
     第三章 廣告推薦系統設計與架構........12
     3.1 系統元件及架構............12
     3.2 系統設計理念...........12
     3.2.1 資訊收集 (Data Collectotion).....13
     3.1.2 內容分析 (Content Analysis)......16
     3.1.3 使用者記錄 (User Profile)........23
     3.1.4 廣告推薦 (Ads Recommendation)....24
     第四章 系統實作............28
     4.1 系統實作相關技術背景.........28
     4.1.1 Microsoft Office Object Library.....28
     4.1.2 SHChangeNotifyRegister API.......30
     4.1.3 Socket Interface...........31
     4.1.4 POP3 Protocol.........32
     4.2 系統實作環境及方法介紹.......34
     4.2.1 .NET Framework........34
     4.2.2 Visual C# Developer.........35
     4.2.3 COM+元件服務.......36
     4.2.4 非同步控制機制.....36
     第五章 實驗與結果..........38
     5.1 收集使用者回應........38
     5.2 實驗設計及方法........39
     5.3 實驗結果及分析........41
     第六章 結論與未來研究方向..........45
     參考文獻..........46
參考文獻 [1] 行政院研究發展考核委員會(民96年11月)，96年數位落差調查報告[公告]，行政院研究發展考核委員會網站，民97年1月，取自：http://www.rdec.gov.tw/DO/DownloadControllerNDO.asp?CuAttachID=12730
     [2] Eager, A., “A Drift on a Sea of Data: Information Overload,” PC User,pp. 17, 1996.
     [3] Katzer, J. and Fletcher, P. T., “The Information Environment of Managers,” Annual Review of Information Science and Technology, Vol. 17, pp. 227-63, 1992.
     [4] Hoffman, D. L., Kalsbeek, W. D., and Novak, T. P.,“Internet and Web Use in the United States: Baselines for Commercial Development,” Communications of the ACM, Vol. 39, No. 12, pp. 36-46.
     [5] Berry, M. J. A., and Linoff, G. S., Data Mining Techniques for Marketing, Sales, and Customer Support, Wiley Computer publishing, 1997.
     [6] Adomavicius, G., and Tuzhilin, A. “Using Data Mining Methods to Build Customer Profiles,” IEEE Computer, Vol. 34, No 2, pp. 74-82, 2001.
     [7] Balabanovic, M. and Shoham, Y., ”Content-Based, Collaborative Recommendation,” Communications of the ACM, 1997.
     [8] Herlocker, J. L., Konstan, J. A., and Riedl, J., “Explaining Collaborative Filtering Recommendations,” Proceedings of the ACM Conference on Computer Supported Cooperative Work, pp. 241-250, 2000.
     [9] Linden, G., Smith, B., and York, J., “Amazon.com Recommendations: Item-to-Item Collaborative Filtering,” IEEE Internet Computing, Vol. 7, No. 1, pp. 76-80, 2003.
     [10] Yih, W., Goodman, J., and Carvalho, V. R., “Finding Advertising Keywords on Web Pages,” Proceedings of the 15th International Conference on World Wide Web, 2006.
     [11] Lacerda, A., Cristo, M., Goncalves M.A., Fan W., Ziviani N., Ribeiro-Neto B,“Learning to Advertise,” Proceedings of the 29th Annual International ACM SIGIR, 2006.
     [12] Schafer, J. B., Konstan, J., and Riedl, J., ”E-Commerce Recommender Applications,” Journal of Data Mining and Knowledge Discovery, Vol. 5, No. 1/2, pp. 115-152, 2000.
     [13] Schafer, J. B., Konstan, J., and Riedl, J., “Recommender System in E-Commerce,” Proceedings of the first ACM conference on Electronic Commerce, pp. 158-166, 1999.
     [14] Ansari, A., Essegaier, S., and Kohli, R., “Internet Recommendation Systems,” Journal of Marketing Research, Vol. 37, No. 3, pp. 363-375, 2000.
     [15] Meteren, R. V., and Someren, M. V., “Using Content-Based Filtering for Recommendation,” Proceedings of ECML Workshop:Machine Learning in New Information Age, pp. 47-56, 2000.
     [16] Chee, S. H. S., Han, J., and Wang, K., “RecTree: An Efficient Collaborative Filtering Method,” Proceedings of International Conference on Data Warehouse and Knowledge Discovery, pp. 141-151, 2001.
     [17] Herlocker, J. L., Konstan, J. A., and Riedl, J., “Explaining Collaborative Filtering Recommendations,” Proceedings of the ACM Conference on Computer Supported Cooperative Work, pp. 241-250, 2000.
     [18] Linden, G., Smith, B., and York, J., “Amazon.com Recommendations: Item-to-Item Collaborative Filtering,” IEEE Internet Computing, Vol. 7, No. 1, pp. 76-80.
     [19] Kim, B. D., and Kim, S. O., “A New Recommender System to Combine Content-Based and Collaborative Filtering Systems,” Journal of Database Marketing, Vol. 8, No. 3, 2001.
     [20] Salton, G., Wong, A., and Yang, C.S., “A Vector Space Model for Automatic Indexing,” Communications of the ACM, Vol. 18, No. 11, pp. 613-620, 1975.
     [21] Salton, G. and Buckley, C. , “Term-weighting Approaches in Automatic Text Retrieval,” Information Processing & Management, Vol. 24, No. 5, pp. 513-523, 1988.
     [22] Aas K., Eikvil.L., “Text Categorization: A Survey,” Norwegian Computing Center Technique Report, No. 941, 1999.
     [23] Salton G. and McGill M., An Introduction to Modern Information Retrieval, New York ; McGraw-Hill, 1983.
     [24] Oard, D.W. and Kim, J., “Implicit Feedback for Recommender Systems,” Proceedings of the AAAI Workshop on Recommender Systems, pp. 81-83, 1998.
     [25] Middleton, S. E., Shadbolt, N. R., “Ontological User Profiling in Recommender Systems,” ACM Trans. Information Systems, Vol. 22, No. 1, pp. 54-88, 2004.
     [26] Herlocker, J. L., Konstan, J. A., Borchers, A., and Riedl, J., “An Algorithmic Framework for Performing Collaborative Filtering,” Proceedings of 22nd Annual International ACM SIGIR, 1999.
     [27] Herlocker, J. L., Konstan, J. A., Terveen, L. G., and Riedl, J.T., ”Evaluating Collaborative Filtering Recommender Systems,” ACM Trans. Information Systems, Vol. 22, No. 1, pp. 5-53, 2004.
     [28] Fawcett, T., “An Introduction to ROC Analysis,” Pattern Recognition Letters, Vol. 27, No. 8, pp. 861-874, 2006.
     [29] Mcnee, S. M., Riedl, J., and Konstan, J. A., “Being Accurate is Not Enough: How Accuracy Metrics Have Hurt Recommender Systems,” Conference on Human Factors in Computing Systems, pp. 1097-1101, 2006.
     [30] Breese, J.S. and Kadie, C., “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” Proceedings of the Conference on Uncertainty in Artificial Intelligence, 1998.
描述 碩士
國立政治大學
資訊科學學系
94971008
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0094971008
資料類型 thesis
dc.contributor.advisor 沈錳坤zh_TW
dc.contributor.advisor Shan, Man Kwanen_US
dc.contributor.author (Authors) 張宏嘉zh_TW
dc.contributor.author (Authors) Chang, Hung Chiaen_US
dc.creator (作者) 張宏嘉zh_TW
dc.creator (作者) Chang, Hung Chiaen_US
dc.date (日期) 2007en_US
dc.date.accessioned 9-May-2016 12:02:24 (UTC+8)-
dc.date.available 9-May-2016 12:02:24 (UTC+8)-
dc.date.issued (上傳時間) 9-May-2016 12:02:24 (UTC+8)-
dc.identifier (Other Identifiers) G0094971008en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/94856-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學學系zh_TW
dc.description (描述) 94971008zh_TW
dc.description.abstract (摘要) 鑑於國人網路依賴度越來越高，每人每日透過網路獲取豐富的資訊及資料，然而由於資訊過多也造成使用者不知道自己真正想要的資訊，因此找尋出個人化資訊勢必成為未來研究的方向。一般網路行銷廣告都是在分析使用者在網站上的行為，然而鮮少去分析到使用者端環境及儲存的資料。相較於使用者在網站上的行為，使用者端所留下的行為資訊更能夠反應出使用者的真正興趣，因此本研究主要探討在個人使用環境(如：PC、NB)中，透過使用者最常接觸的三種途徑(包括：上網瀏覽資訊、信件資訊及常用檔案內容資訊)來獲取使用者興趣的資訊，並且建構出一套個人化廣告推薦系統常駐於使用者端即時(real-time)記錄使用者的行為資訊。本系統運用推薦技術(Recommendation Technique)搭配關聯式法則(Association Rule)來將這些資訊有效的過濾並關聯出使用者的喜好及興趣，同時利用這些資訊上網找尋合適的廣告資料，用以建構出個人化廣告推薦模式。zh_TW
dc.description.abstract (摘要) The rapid growth of Internet has changed the patterns of our life. Everyone can gain rich information on internet, but plenty of information will confuse user`s ability to determine whether these information are useful. Therefore, the further trend is to discover personalized information. Many researches about internet marketing advertisement are mining user`s behavior in website server, but scarce researches focus on client. Compared to user`s behavior in website, the behavior information which stays in local environment (ex. PC, NB) can reflect user`s profile more. Thus, this research mainly discuss how to record user`s behavior information containing Web Page Title, E-mail Subject and Document Content in local environment and how to construct a personalized advertisement recommendation system resident in memory of local environment for timely (On-line) collecting user`s behavior information to create “user profile” using recommendation technique and association rule. This system will utilize “user profile” to provide appropriate personalized advertisement for user. Finally, we apply several experiments to verify the feasibility of our system.en_US
dc.description.abstract (摘要) 第一章 導論............1
     1.1 研究背景..........1
     1.2 研究動機..........2
     1.3 研究問題與目的....2
     第二章 技術背景與相關研究.........4
     2.1 An Introduction to Recommendation........4
     2.2 Recommendation Approach...........5
     2.2.1 內容導向式（Content-based Filtering）.........5
     2.2.2 協同過濾式 （Collaborative Filtering, CF）....8
     2.2.3 混合式 （Hybrid-based filtering）.........9
     2.3 Extensions for Recommendation Techniques...10
     第三章 廣告推薦系統設計與架構........12
     3.1 系統元件及架構............12
     3.2 系統設計理念...........12
     3.2.1 資訊收集 (Data Collectotion).....13
     3.1.2 內容分析 (Content Analysis)......16
     3.1.3 使用者記錄 (User Profile)........23
     3.1.4 廣告推薦 (Ads Recommendation)....24
     第四章 系統實作............28
     4.1 系統實作相關技術背景.........28
     4.1.1 Microsoft Office Object Library.....28
     4.1.2 SHChangeNotifyRegister API.......30
     4.1.3 Socket Interface...........31
     4.1.4 POP3 Protocol.........32
     4.2 系統實作環境及方法介紹.......34
     4.2.1 .NET Framework........34
     4.2.2 Visual C# Developer.........35
     4.2.3 COM+元件服務.......36
     4.2.4 非同步控制機制.....36
     第五章 實驗與結果..........38
     5.1 收集使用者回應........38
     5.2 實驗設計及方法........39
     5.3 實驗結果及分析........41
     第六章 結論與未來研究方向..........45
     參考文獻..........46		-
dc.description.tableofcontents 第一章 導論............1
      1.1 研究背景..........1
      1.2 研究動機..........2
      1.3 研究問題與目的....2
     第二章 技術背景與相關研究.........4
      2.1 An Introduction to Recommendation........4
      2.2 Recommendation Approach...........5
      2.2.1 內容導向式（Content-based Filtering）.........5
      2.2.2 協同過濾式 （Collaborative Filtering, CF）....8
      2.2.3 混合式 （Hybrid-based filtering）.........9
      2.3 Extensions for Recommendation Techniques...10
     第三章 廣告推薦系統設計與架構........12
      3.1 系統元件及架構............12
      3.2 系統設計理念...........12
      3.2.1 資訊收集 (Data Collectotion).....13
      3.1.2 內容分析 (Content Analysis)......16
      3.1.3 使用者記錄 (User Profile)........23
      3.1.4 廣告推薦 (Ads Recommendation)....24
     第四章 系統實作............28
      4.1 系統實作相關技術背景.........28
      4.1.1 Microsoft Office Object Library.....28
      4.1.2 SHChangeNotifyRegister API.......30
      4.1.3 Socket Interface...........31
      4.1.4 POP3 Protocol.........32
      4.2 系統實作環境及方法介紹.......34
      4.2.1 .NET Framework........34
      4.2.2 Visual C# Developer.........35
      4.2.3 COM+元件服務.......36
      4.2.4 非同步控制機制.....36
     第五章 實驗與結果..........38
      5.1 收集使用者回應........38
      5.2 實驗設計及方法........39
      5.3 實驗結果及分析........41
     第六章 結論與未來研究方向..........45
     參考文獻..........46		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0094971008en_US
dc.subject (關鍵詞) 個人化zh_TW
dc.subject (關鍵詞) 廣告zh_TW
dc.subject (關鍵詞) 推薦系統zh_TW
dc.subject (關鍵詞) Personalizeden_US
dc.subject (關鍵詞) Advertisementen_US
dc.subject (關鍵詞) Recommendar Systemen_US
dc.title (題名) 個人化廣告推薦軟體之設計與實做zh_TW
dc.title (題名) The Design and implementations of a personalized advertisement recommendation softwareen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1] 行政院研究發展考核委員會(民96年11月)，96年數位落差調查報告[公告]，行政院研究發展考核委員會網站，民97年1月，取自：http://www.rdec.gov.tw/DO/DownloadControllerNDO.asp?CuAttachID=12730
     [2] Eager, A., “A Drift on a Sea of Data: Information Overload,” PC User,pp. 17, 1996.
     [3] Katzer, J. and Fletcher, P. T., “The Information Environment of Managers,” Annual Review of Information Science and Technology, Vol. 17, pp. 227-63, 1992.
     [4] Hoffman, D. L., Kalsbeek, W. D., and Novak, T. P.,“Internet and Web Use in the United States: Baselines for Commercial Development,” Communications of the ACM, Vol. 39, No. 12, pp. 36-46.
     [5] Berry, M. J. A., and Linoff, G. S., Data Mining Techniques for Marketing, Sales, and Customer Support, Wiley Computer publishing, 1997.
     [6] Adomavicius, G., and Tuzhilin, A. “Using Data Mining Methods to Build Customer Profiles,” IEEE Computer, Vol. 34, No 2, pp. 74-82, 2001.
     [7] Balabanovic, M. and Shoham, Y., ”Content-Based, Collaborative Recommendation,” Communications of the ACM, 1997.
     [8] Herlocker, J. L., Konstan, J. A., and Riedl, J., “Explaining Collaborative Filtering Recommendations,” Proceedings of the ACM Conference on Computer Supported Cooperative Work, pp. 241-250, 2000.
     [9] Linden, G., Smith, B., and York, J., “Amazon.com Recommendations: Item-to-Item Collaborative Filtering,” IEEE Internet Computing, Vol. 7, No. 1, pp. 76-80, 2003.
     [10] Yih, W., Goodman, J., and Carvalho, V. R., “Finding Advertising Keywords on Web Pages,” Proceedings of the 15th International Conference on World Wide Web, 2006.
     [11] Lacerda, A., Cristo, M., Goncalves M.A., Fan W., Ziviani N., Ribeiro-Neto B,“Learning to Advertise,” Proceedings of the 29th Annual International ACM SIGIR, 2006.
     [12] Schafer, J. B., Konstan, J., and Riedl, J., ”E-Commerce Recommender Applications,” Journal of Data Mining and Knowledge Discovery, Vol. 5, No. 1/2, pp. 115-152, 2000.
     [13] Schafer, J. B., Konstan, J., and Riedl, J., “Recommender System in E-Commerce,” Proceedings of the first ACM conference on Electronic Commerce, pp. 158-166, 1999.
     [14] Ansari, A., Essegaier, S., and Kohli, R., “Internet Recommendation Systems,” Journal of Marketing Research, Vol. 37, No. 3, pp. 363-375, 2000.
     [15] Meteren, R. V., and Someren, M. V., “Using Content-Based Filtering for Recommendation,” Proceedings of ECML Workshop:Machine Learning in New Information Age, pp. 47-56, 2000.
     [16] Chee, S. H. S., Han, J., and Wang, K., “RecTree: An Efficient Collaborative Filtering Method,” Proceedings of International Conference on Data Warehouse and Knowledge Discovery, pp. 141-151, 2001.
     [17] Herlocker, J. L., Konstan, J. A., and Riedl, J., “Explaining Collaborative Filtering Recommendations,” Proceedings of the ACM Conference on Computer Supported Cooperative Work, pp. 241-250, 2000.
     [18] Linden, G., Smith, B., and York, J., “Amazon.com Recommendations: Item-to-Item Collaborative Filtering,” IEEE Internet Computing, Vol. 7, No. 1, pp. 76-80.
     [19] Kim, B. D., and Kim, S. O., “A New Recommender System to Combine Content-Based and Collaborative Filtering Systems,” Journal of Database Marketing, Vol. 8, No. 3, 2001.
     [20] Salton, G., Wong, A., and Yang, C.S., “A Vector Space Model for Automatic Indexing,” Communications of the ACM, Vol. 18, No. 11, pp. 613-620, 1975.
     [21] Salton, G. and Buckley, C. , “Term-weighting Approaches in Automatic Text Retrieval,” Information Processing & Management, Vol. 24, No. 5, pp. 513-523, 1988.
     [22] Aas K., Eikvil.L., “Text Categorization: A Survey,” Norwegian Computing Center Technique Report, No. 941, 1999.
     [23] Salton G. and McGill M., An Introduction to Modern Information Retrieval, New York ; McGraw-Hill, 1983.
     [24] Oard, D.W. and Kim, J., “Implicit Feedback for Recommender Systems,” Proceedings of the AAAI Workshop on Recommender Systems, pp. 81-83, 1998.
     [25] Middleton, S. E., Shadbolt, N. R., “Ontological User Profiling in Recommender Systems,” ACM Trans. Information Systems, Vol. 22, No. 1, pp. 54-88, 2004.
     [26] Herlocker, J. L., Konstan, J. A., Borchers, A., and Riedl, J., “An Algorithmic Framework for Performing Collaborative Filtering,” Proceedings of 22nd Annual International ACM SIGIR, 1999.
     [27] Herlocker, J. L., Konstan, J. A., Terveen, L. G., and Riedl, J.T., ”Evaluating Collaborative Filtering Recommender Systems,” ACM Trans. Information Systems, Vol. 22, No. 1, pp. 5-53, 2004.
     [28] Fawcett, T., “An Introduction to ROC Analysis,” Pattern Recognition Letters, Vol. 27, No. 8, pp. 861-874, 2006.
     [29] Mcnee, S. M., Riedl, J., and Konstan, J. A., “Being Accurate is Not Enough: How Accuracy Metrics Have Hurt Recommender Systems,” Conference on Human Factors in Computing Systems, pp. 1097-1101, 2006.
     [30] Breese, J.S. and Kadie, C., “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” Proceedings of the Conference on Uncertainty in Artificial Intelligence, 1998.		zh_TW