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| Title | 多維度行事曆助理 |
| Creator | 張文祥 |
| Contributor | 楊亨利 張文祥 |
| Key Words | 推薦系統 助理軟體 多維度推薦 個人化行事曆 Recommender system intelligent assistant software multi-dimension recommendation individualized journey |
| Date | 2005 |
| Date Issued | 18-Sep-2009 14:29:05 (UTC+8) |
| Summary | 隨著資訊科技的發展，網際網路成為個人獲得資訊的主要來源之一。但是過多的資訊產生資訊爆炸(information overload)的現象，人們除了要在眾多資訊中找尋想要的資訊外，還需要擔心所尋找到的資訊的品質是否良好。因此，推薦系統提供了一個良好的解決方法。推薦系統透過分群與推薦的技術來達到減少資訊量與推估使用者潛在興趣的目的。目前推薦系統多應用在單一維度的推薦，本論文希望藉由某一情境來探討多維度推薦的應用，所以選擇助理軟體來實現多維度推薦的應用。選擇助理軟體是由於其已經成為個人日常生活中時常使用的工具，且由於助理軟體管理個人日常生活中的大小事務，成為最貼近個人的工具。若專注在個人行事曆的安排上，我們可以發現個人行事曆安排牽涉到有人、事、時、地、物五個維度。因此我們以五維度做分群，透過合作推薦(Collaborative Recommender)的方式將可以達到個人潛在興趣的多維度(Multi-Dimensions)推薦。本研究將以行事歷排定為情境，來說明如何將五個維度的各種可能組合依照其契合個人興趣的程度來進行推薦，這將使得助理軟體的內容更加豐富，且能貼近使用者的需求，提供意想不到的資訊組合。 With the development of information science and technology, assistant software becomes a tool which often uses in personal daily life, and because all kinds of affairs in personal daily life that assistant software is managed, so assistant software becomes a tool which personally close to people. Intelligent assistant software hopes to make assistant software have intelligence which is similar to the mankind. Just like a personal general secretary, arrange the most proper individualized journey. Further, it can combine the idea of Recommender system to recommend the journey of the potential interest while arranging in the personal journey. This research proposes an intelligent assistant software with five- dimensions include of people, thing, when, location and things, uses cooperative Recommender approach to reach multi-dimension recommendation of personal potential interest. This research will give example of meeting as the situation to explain how to make five-dimensions recommendation according to personal interest. This will make the content of assistant software more abundant, and can press close to the user`s demand. |
| 參考文獻 | 英文部分 1. Adomavicius, G., and A. Tuzhilin (2001), “Using Data Mining Methods to Build Customer Profiles,” Computer 34(2),pp.74-82. 2. Allen, C., D. Kania, and B. Yaeckel (1998), “Internet World Guide to One-To-One Web Marketing,” New York : John Wiley & Sons. 3. Balabanovic, M. and Y. Shoham (1997), “Fab: Content-based, Collaborative Recommendation,” Communications of the ACM, 40, pp.66-72. 4. Claypool, M. and A. Gokhale (1999), “Combining Content-based and Collaborative Filters in an Online Newspaper,” Workshop on Recommender System:Algorithems and Evaluation. 5. Dean, R. (1998), Personalizing your web site, available at http://www.builder.com/business/personal. 6. Delgado, J., N. Ishii , and T. Ura (1998), “Cotent-based Collaborative Information Filtering:Actively Learning to Classify and Recommend Documents,” In Proc. Second Int. Workshop, CIA’98. 7. Goldberg, D., D. Nichols, B.M. Oki, and D. Terry (1992), “Using Collaborative Filtering to Weave an Information Tapestry,” Communications of the ACM, 35(12). 8. Good, N., J. Schafer, J. Konstan, A. Borchers, B. Sarwar, J. Herlocker, and J. Riedl (1999), “Combining Collaborative Filtering with Personal Agents for Better Recommendations,” In Proceedings of the Sixteenth National Conference on Artificial Intelligence. 9. Han, J., Y. Cai, and N. Cercone (1992) , “Knowledge Discovery in Databases: an Attribute-oriented Approach,” Proceeding of the 18th VLDB Conference, Canada, August, pp.547-549. 10. Han J., M. Kamber (2001), “Data Mining: Concepts and Techniques,” Classification and Prediction of Chapter 7. San Francisco, California, Morgan Kaufmann Publishers, pp.279-325. 11. Hanani, U., B. Shapira and P. Shoval (2001), “Information Filtering:Overview of Issues, Research and Systems,” User Modeling and User-Adapted Interaction 11(3), pp.59-203. 12. Hill, W., L. Stead , M. Rosentein and G. W. Furnas (1995), “Recommending and Evaluating Choices in a Virtual Community of Use,” In Proceedings of ACM CHI’95 Conference on Human Factors in Computing Systems. ACM,New York, pp.194-201. 13. Hoffman, D.L., W.D. Kalsbeek, and T.P. Novak (1996), “Internet and Web Use in the United States:Baselines for Commercial Development,” Communication of ACM 39(12), pp.36-46. 14. Jennings, N. R. and A. J. Jackson (1995), “Agent Based Meeting Scheduling: a Design and Implementation,” IEE Electronics Letters Journal. 15. Kozierok, R. and P. Maes (1993), “A Learning Interface Agent for Scheduling Meetings,” Proceedings of the 1993 International Workshop on Intelligent User Interfaces, New York: ACM Press, pp.81-96. 16. Lewis, D. (1996), “Dying for Information: an Investigation of the Effects of Information Overload in the UK and World-wide,” London: Reuters. 17. Middleton, S.E. (2001), “Interface Agents: a Review of the Field,” Technical Report Number: ECSTR-IAM01-001,” University of Southampton. 18. McDonald, D. W. (2003), “Ubiquitous Recommendation System,” Computer, 36(10), pp.111-112. 19. Mitchell, T., R. Caruana, J. McDermott and D. Zabowski (1994), “Experience With a Learning Personal Assistant,” Communications of the ACM, 37(7). 20. Sarwar, B. M., G. Karypis, J. A. Konstan, and J. Riedl (2001), “Item-based Collaborative Filtering Recommendation Algorithms,” WWW10, May 1-5, Hong Kong. 21. Schafer, J.B., J.A. Konstan and J. Riedl (1999), “Recommender Systems in E-commerce,” ACM Conference on Electronic Commerce (EC-99), pp. 158-166. 22. Schafer J.B., J.A. Konstan and J. Riedl (2000), ”E-Commerence Recommendation Application,” Data Mining and Knowledge Discovery. 23. Shardanand, U. and P. Maes (1995), “Social Information Filtering:Algorithms for Automating:Wordof Mounth,” In Proceedings of ACM CHI’95 Conference on Human Factors in Computing Systems. ACM,New York, pp.210-217. 24. Chung-Ching Yu (2002), “Mining Sequential Patterns from Multi-Dimensional Sequence Data,” 國立中央大學資訊管理研究所碩士論文. 25. Wasfi, A. M. A. (1999), “Collecting User Access Patterns for Building user Profiles and Collaborative Filtering,” In Int. Conf. On Intelligent User Interfaces. 中文部份 26. 莊士民(2003)，“Combining Context-Based and Collaborative Article Recommendation in Literature Digital Libraries”，國立中山大學資訊管理研究所碩士論文，2003。 27. 莊美娟(2002)，“Mining the Inter-Transactional Association Rules of Multi-Dimension Interval Patterns”，國立台灣大學資訊管理研究所碩士論文。 28. 徐明哲(1993)，“圖書館個人化館藏推薦系統”，國立交通大學資訊科學研究所碩士論文。 29. 陳柏翰(2005)，”個人化線上求職推薦系統之研究”，私立中國文化大學資訊管理研究所碩士論文。 30. 陳復亘(2005)，” 台灣商務型網站使用推薦系統現況之研究”， 私立中國文化大學資訊管理研究所碩士論文。 31. 陳鴻新(2005)，”建構一個案例商議模式的推薦系統-以IC測試業服務內容推薦為例”，私立華梵大學資訊管理碩士班。 32. 曾靖茹(2003)，“Cluster-Based Collaborative Filtering Recommendation Approach”，國立中山大學資訊管理研究所碩士論文。 |
| Description | 碩士 國立政治大學 資訊管理研究所 93356015 94 |
| 資料來源 | http://thesis.lib.nccu.edu.tw/record/#G0093356015 |
| Type | thesis |
| dc.contributor.advisor | 楊亨利 | zh_TW |
| dc.contributor.author (Authors) | 張文祥 | zh_TW |
| dc.creator (作者) | 張文祥 | zh_TW |
| dc.date (日期) | 2005 | en_US |
| dc.date.accessioned | 18-Sep-2009 14:29:05 (UTC+8) | - |
| dc.date.available | 18-Sep-2009 14:29:05 (UTC+8) | - |
| dc.date.issued (上傳時間) | 18-Sep-2009 14:29:05 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0093356015 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/35222 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 資訊管理研究所 | zh_TW |
| dc.description (描述) | 93356015 | zh_TW |
| dc.description (描述) | 94 | zh_TW |
| dc.description.abstract (摘要) | 隨著資訊科技的發展，網際網路成為個人獲得資訊的主要來源之一。但是過多的資訊產生資訊爆炸(information overload)的現象，人們除了要在眾多資訊中找尋想要的資訊外，還需要擔心所尋找到的資訊的品質是否良好。因此，推薦系統提供了一個良好的解決方法。推薦系統透過分群與推薦的技術來達到減少資訊量與推估使用者潛在興趣的目的。目前推薦系統多應用在單一維度的推薦，本論文希望藉由某一情境來探討多維度推薦的應用，所以選擇助理軟體來實現多維度推薦的應用。選擇助理軟體是由於其已經成為個人日常生活中時常使用的工具，且由於助理軟體管理個人日常生活中的大小事務，成為最貼近個人的工具。若專注在個人行事曆的安排上，我們可以發現個人行事曆安排牽涉到有人、事、時、地、物五個維度。因此我們以五維度做分群，透過合作推薦(Collaborative Recommender)的方式將可以達到個人潛在興趣的多維度(Multi-Dimensions)推薦。本研究將以行事歷排定為情境，來說明如何將五個維度的各種可能組合依照其契合個人興趣的程度來進行推薦，這將使得助理軟體的內容更加豐富，且能貼近使用者的需求，提供意想不到的資訊組合。 | zh_TW |
| dc.description.abstract (摘要) | With the development of information science and technology, assistant software becomes a tool which often uses in personal daily life, and because all kinds of affairs in personal daily life that assistant software is managed, so assistant software becomes a tool which personally close to people. Intelligent assistant software hopes to make assistant software have intelligence which is similar to the mankind. Just like a personal general secretary, arrange the most proper individualized journey. Further, it can combine the idea of Recommender system to recommend the journey of the potential interest while arranging in the personal journey. This research proposes an intelligent assistant software with five- dimensions include of people, thing, when, location and things, uses cooperative Recommender approach to reach multi-dimension recommendation of personal potential interest. This research will give example of meeting as the situation to explain how to make five-dimensions recommendation according to personal interest. This will make the content of assistant software more abundant, and can press close to the user`s demand. | en_US |
| dc.description.tableofcontents | 摘要 2 ABSTRACT 3 1.緒論 8 1.1 研究背景 8 1.2研究動機與目的 9 1.3研究流程 10 2.文獻探討 11 2.1個人化 11 2.1.1個人化的程度 11 2.1.2 個人化的程序 11 2.1.3個人化資訊的取得 12 2.2推薦系統 13 2.2.1 推薦系統簡介 13 2.2.2 推薦方法的優缺點與混合式推薦系統 15 2.2.3 使用者Profile 16 2.2.4 多維度推薦系統與現存推薦系統比較 17 2.2.5相似度測量方法 18 2.3個人化助理軟體 19 2.4分群介紹 20 2.5 小結 21 3.系統架構 23 3.1 分類樹 23 3.2概念樹資料庫介紹 29 3.3系統模組介紹 32 3.3.1 Profile建構模組 33 3.3.2 分群模組 34 3.3.3 行事曆模組 40 3.3.4 推薦功能模組 41 3.4功能流程 48 3.5分群與推薦方法的特色 49 3.6情境說明 49 3.7系統架構特色 50 3.8小結 51 4.系統建置 52 4.1系統規劃 52 4.2管理者介面 55 目標分群後將會把無法利用實際資料分群的使用者使用目標比例分群，分群後的使用者將會與目前的使用者分群，隨後將可以利用此相似度進行推薦 58 4.3使用者介面 59 4.4小結 72 5.結論與建議 73 5.1研究結論與貢獻 73 5.2研究限制 74 5.3後續研究 75 參考文獻 76 英文部分 76 中文部份 78 | zh_TW |
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| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0093356015 | en_US |
| dc.subject (關鍵詞) | 推薦系統 | zh_TW |
| dc.subject (關鍵詞) | 助理軟體 | zh_TW |
| dc.subject (關鍵詞) | 多維度推薦 | zh_TW |
| dc.subject (關鍵詞) | 個人化行事曆 | zh_TW |
| dc.subject (關鍵詞) | Recommender system | en_US |
| dc.subject (關鍵詞) | intelligent assistant software | en_US |
| dc.subject (關鍵詞) | multi-dimension recommendation | en_US |
| dc.subject (關鍵詞) | individualized journey | en_US |
| dc.title (題名) | 多維度行事曆助理 | zh_TW |
| dc.type (資料類型) | thesis | en |
| dc.relation.reference (參考文獻) | 英文部分 | zh_TW |
| dc.relation.reference (參考文獻) | 1. Adomavicius, G., and A. Tuzhilin (2001), “Using Data Mining Methods to Build Customer Profiles,” Computer 34(2),pp.74-82. | zh_TW |
| dc.relation.reference (參考文獻) | 2. Allen, C., D. Kania, and B. Yaeckel (1998), “Internet World Guide to One-To-One Web Marketing,” New York : John Wiley & Sons. | zh_TW |
| dc.relation.reference (參考文獻) | 3. Balabanovic, M. and Y. Shoham (1997), “Fab: Content-based, Collaborative Recommendation,” Communications of the ACM, 40, pp.66-72. | zh_TW |
| dc.relation.reference (參考文獻) | 4. Claypool, M. and A. Gokhale (1999), “Combining Content-based and Collaborative Filters in an Online Newspaper,” Workshop on Recommender System:Algorithems and Evaluation. | zh_TW |
| dc.relation.reference (參考文獻) | 5. Dean, R. (1998), Personalizing your web site, available at http://www.builder.com/business/personal. | zh_TW |
| dc.relation.reference (參考文獻) | 6. Delgado, J., N. Ishii , and T. Ura (1998), “Cotent-based Collaborative Information Filtering:Actively Learning to Classify and Recommend Documents,” In Proc. Second Int. Workshop, CIA’98. | zh_TW |
| dc.relation.reference (參考文獻) | 7. Goldberg, D., D. Nichols, B.M. Oki, and D. Terry (1992), “Using Collaborative Filtering to Weave an Information Tapestry,” Communications of the ACM, 35(12). | zh_TW |
| dc.relation.reference (參考文獻) | 8. Good, N., J. Schafer, J. Konstan, A. Borchers, B. Sarwar, J. Herlocker, and J. Riedl (1999), “Combining Collaborative Filtering with Personal Agents for Better Recommendations,” In Proceedings of the Sixteenth National Conference on Artificial Intelligence. | zh_TW |
| dc.relation.reference (參考文獻) | 9. Han, J., Y. Cai, and N. Cercone (1992) , “Knowledge Discovery in Databases: an Attribute-oriented Approach,” Proceeding of the 18th VLDB Conference, Canada, August, pp.547-549. | zh_TW |
| dc.relation.reference (參考文獻) | 10. Han J., M. Kamber (2001), “Data Mining: Concepts and Techniques,” Classification and Prediction of Chapter 7. San Francisco, California, Morgan Kaufmann Publishers, pp.279-325. | zh_TW |
| dc.relation.reference (參考文獻) | 11. Hanani, U., B. Shapira and P. Shoval (2001), “Information Filtering:Overview of Issues, Research and Systems,” User Modeling and User-Adapted Interaction 11(3), pp.59-203. | zh_TW |
| dc.relation.reference (參考文獻) | 12. Hill, W., L. Stead , M. Rosentein and G. W. Furnas (1995), “Recommending and Evaluating Choices in a Virtual Community of Use,” In Proceedings of ACM CHI’95 Conference on Human Factors in Computing Systems. ACM,New York, pp.194-201. | zh_TW |
| dc.relation.reference (參考文獻) | 13. Hoffman, D.L., W.D. Kalsbeek, and T.P. Novak (1996), “Internet and Web Use in the United States:Baselines for Commercial Development,” Communication of ACM 39(12), pp.36-46. | zh_TW |
| dc.relation.reference (參考文獻) | 14. Jennings, N. R. and A. J. Jackson (1995), “Agent Based Meeting Scheduling: a Design and Implementation,” IEE Electronics Letters Journal. | zh_TW |
| dc.relation.reference (參考文獻) | 15. Kozierok, R. and P. Maes (1993), “A Learning Interface Agent for Scheduling Meetings,” Proceedings of the 1993 International Workshop on Intelligent User Interfaces, New York: ACM Press, pp.81-96. | zh_TW |
| dc.relation.reference (參考文獻) | 16. Lewis, D. (1996), “Dying for Information: an Investigation of the Effects of Information Overload in the UK and World-wide,” London: Reuters. | zh_TW |
| dc.relation.reference (參考文獻) | 17. Middleton, S.E. (2001), “Interface Agents: a Review of the Field,” Technical Report Number: ECSTR-IAM01-001,” University of Southampton. | zh_TW |
| dc.relation.reference (參考文獻) | 18. McDonald, D. W. (2003), “Ubiquitous Recommendation System,” Computer, 36(10), pp.111-112. | zh_TW |
| dc.relation.reference (參考文獻) | 19. Mitchell, T., R. Caruana, J. McDermott and D. Zabowski (1994), “Experience With a Learning Personal Assistant,” Communications of the ACM, 37(7). | zh_TW |
| dc.relation.reference (參考文獻) | 20. Sarwar, B. M., G. Karypis, J. A. Konstan, and J. Riedl (2001), “Item-based Collaborative Filtering Recommendation Algorithms,” WWW10, May 1-5, Hong Kong. | zh_TW |
| dc.relation.reference (參考文獻) | 21. Schafer, J.B., J.A. Konstan and J. Riedl (1999), “Recommender Systems in E-commerce,” ACM Conference on Electronic Commerce (EC-99), pp. 158-166. | zh_TW |
| dc.relation.reference (參考文獻) | 22. Schafer J.B., J.A. Konstan and J. Riedl (2000), ”E-Commerence Recommendation Application,” Data Mining and Knowledge Discovery. | zh_TW |
| dc.relation.reference (參考文獻) | 23. Shardanand, U. and P. Maes (1995), “Social Information Filtering:Algorithms for Automating:Wordof Mounth,” In Proceedings of ACM CHI’95 Conference on Human Factors in Computing Systems. ACM,New York, pp.210-217. | zh_TW |
| dc.relation.reference (參考文獻) | 24. Chung-Ching Yu (2002), “Mining Sequential Patterns from Multi-Dimensional Sequence Data,” 國立中央大學資訊管理研究所碩士論文. | zh_TW |
| dc.relation.reference (參考文獻) | 25. Wasfi, A. M. A. (1999), “Collecting User Access Patterns for Building user Profiles and Collaborative Filtering,” In Int. Conf. On Intelligent User Interfaces. | zh_TW |
| dc.relation.reference (參考文獻) | 中文部份 | zh_TW |
| dc.relation.reference (參考文獻) | 26. 莊士民(2003)，“Combining Context-Based and Collaborative Article Recommendation in Literature Digital Libraries”，國立中山大學資訊管理研究所碩士論文，2003。 | zh_TW |
| dc.relation.reference (參考文獻) | 27. 莊美娟(2002)，“Mining the Inter-Transactional Association Rules of Multi-Dimension Interval Patterns”，國立台灣大學資訊管理研究所碩士論文。 | zh_TW |
| dc.relation.reference (參考文獻) | 28. 徐明哲(1993)，“圖書館個人化館藏推薦系統”，國立交通大學資訊科學研究所碩士論文。 | zh_TW |
| dc.relation.reference (參考文獻) | 29. 陳柏翰(2005)，”個人化線上求職推薦系統之研究”，私立中國文化大學資訊管理研究所碩士論文。 | zh_TW |
| dc.relation.reference (參考文獻) | 30. 陳復亘(2005)，” 台灣商務型網站使用推薦系統現況之研究”， 私立中國文化大學資訊管理研究所碩士論文。 | zh_TW |
| dc.relation.reference (參考文獻) | 31. 陳鴻新(2005)，”建構一個案例商議模式的推薦系統-以IC測試業服務內容推薦為例”，私立華梵大學資訊管理碩士班。 | zh_TW |
| dc.relation.reference (參考文獻) | 32. 曾靖茹(2003)，“Cluster-Based Collaborative Filtering Recommendation Approach”，國立中山大學資訊管理研究所碩士論文。 | zh_TW |