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    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/35222


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    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
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    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.
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    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.
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    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.
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    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.
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    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

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