政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/35222
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文笔数/总笔数 : 110941/141868 (78%)
造访人次 : 47504265      在线人数 : 1064
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 查询小技巧：
  • 您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果
  • 若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据
    •  进阶搜寻
    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/35222


    请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/35222


    题名: 多維度行事曆助理
    作者: 張文祥
    贡献者: 楊亨利
    張文祥
    关键词: 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
    日期: 2005
    上传时间: 2009-09-18 14:29:05 (UTC+8)
    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。
    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。
    描述: 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
    数据类型: thesis
    显示于类别:[資訊管理學系] 學位論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    35601501.pdf43KbAdobe PDF2637检视/开启
    35601502.pdf85KbAdobe PDF2664检视/开启
    35601503.pdf69KbAdobe PDF2665检视/开启
    35601504.pdf66KbAdobe PDF2638检视/开启
    35601505.pdf105KbAdobe PDF2929检视/开启
    35601506.pdf177KbAdobe PDF22008检视/开启
    35601507.pdf287KbAdobe PDF21021检视/开启
    35601508.pdf1563KbAdobe PDF2802检视/开启
    35601509.pdf101KbAdobe PDF2823检视/开启
    35601510.pdf64KbAdobe PDF21054检视/开启


    在政大典藏中所有的数据项都受到原著作权保护.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈