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研究生:楊宗憲
研究生(外文):Tsung-Hsien Yang
論文名稱:智慧型代理人於個人化多媒體頻道選擇之研究
論文名稱(外文):An Intelligent Agent-Based System for Personalized Channel Selection in Multi-media Broadcasting Environments
指導教授:李偉柏
指導教授(外文):Wei-Po Lee
學位類別:碩士
校院名稱:國立屏東科技大學
系所名稱:資訊管理系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:64
中文關鍵詞:資訊家電智慧型代理人個人化推薦系統決策樹
外文關鍵詞:Information AppliancesIntelligent AgentPersonalized Recommender SystemDecision Tree
相關次數:
  • 被引用被引用:6
  • 點閱點閱:454
  • 評分評分:
  • 下載下載:102
  • 收藏至我的研究室書目清單書目收藏:3
論文摘要內容:
資訊家電(IA)產品的興起,帶動了一股家電連網的風潮。數位電視、Set-Top Box等IA產品不但提升了電視接收資訊的能力,也複雜了使用者對於節目的選擇,造成資訊過量的問題,而個人化的推薦系統是解決資訊過量的好方法。本研究探討如何在具有時間性的多媒體頻道上進行及時性的節目推薦。依據代理人的特性:自主性、適應性、主動性及社會性,以代理人為基礎(Agent-Based)建構出一個能自動監控、學習、排程、回饋的個人化推薦系統。本系統結合Content-Based Filtering 與Collaborative Filtering的推薦方式,增加使用者的興趣方向與提高推薦的正確率。系統的學習代理人採用決策樹演算法能有效的分類使用者興趣;排程代理人能歸納出使用者的習慣排程;介面代理人能記錄使用者行為,以供系統重學。為證明系統的機制效能,本研究進行了多種實驗,以驗證及評估系統績效。
Abstract:
It has been advocated to develop information appliances to provide ubiquitous Internet information access. With the invention of digital set-top-box, television is expected to become one of the most popular information appliances soon, due to the tremendous digital multi-media programmes it can broadcast. However, as in the World Wide Web, the available TV programmes and their correspondingly electronic information within the increasing digital channels lead to the problem of information overload. Though the audience has more alternatives while choosing programmes, he also has to spend more and more time to read the on-line information about the programme contents or browse different channels in order to decide what to watch. One way to overcome such a problem is to build intelligent recommender systems to provide personalized information services. By analyzing the information collected from the user, a personalized recommender system is able to reason his personal preferences and then choose the programmes for him. This paper presents a multi-agent framework in which a decision tree-based approach is proposed to learn a user’s preferences. The experimental studies concentrate on how to recommend film and news programmes to a user, and on how the system can adapt to a user’s most recent preferences. The results and analysis show the promise of our system.
目 錄
摘 要 I
Abstract II
誌 謝 III
目 錄 IV
圖目錄 VI
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 4
1.4 研究步驟 6
第二章 文獻探討 8
2.1 資訊家電 8
2.1.1 資訊家電的定義 8
2.1.2 資訊家電興起的原因 8
2.1.3 網路電視 9
2.2 推薦系統 10
2.2.1 推薦系統的分類 10
2.2.2 Collaborative Filtering推薦系統 12
2.2.3 目前較著名的網站推薦系統 16
2.3 智慧型代理人 19
2.3.1 智慧型代理人的定義 19
2.3.2 智慧型代理人的特性 21
第三章 系統架構與推薦流程 22
3.1 系統架構 23
3.2 推薦流程 27
第四章 系統學習方法與流程 31
4.1 決策樹 31
4.1.1 ID3演算法的建置步驟 34
4.1.2 修剪決策樹 35
4.2 k-NN演算法 36
4.3 學習流程 37
第五章 實驗步驟與結果 42
5.1 電影資料來源:All movie Guide網站 42
5.2 新聞資料來源:CNN新聞資料 55
第六章 結論與未來研究 57
6.1 結論 57
6.2 未來研究 58
參考文獻 60
作者簡介 64
參考文獻
中文部分:
[1] 徐義建,由技術與市場需求來探討智慧型手持式裝置之發展研究---以科學園區為例。國立交通大學科技管理研究所碩士論文,2001。
[2] 王嫦瑛,ABI預測數位電視接取產品市場將於2003年開始快速起飛。經濟部ITIS計劃,1999。
英文部分:
[3] Ian H. Witten, Eibe Frank. Data Mining-Practical Machine Learning Tools and Techniques with Java Implementations. MORGAN KAUFMANN PUBLISHERS, 2000.
[4] Michael J. A. Berry, Gordon Linoff. Data Mining Techniques: for marketing, sales, and customer support. Arrangement with WeiKegPublishing Co. 1997.
[5] N. Good, J. B. Schafer, J, A. Konstan, A. Borchers, B. Sarwar,J. Herlocker, and J. Riedl. Combining Collaborative Filtering with Personal Agents for Better Recommendations. In proceedings of National Conference on Artificial Intelligence (AAAI-99), pp.439-446, 1999.
[6] C. Basu, H. Hirsh, W. Cohen.Recommendation as Classification: Using Social and Content-Based Information in Recommendation. In Proceedings of National Conference on Artificial Intelligence (AAAI-98), pp.714-720, 1998.
[7] P. Cotter, B. Smyth. PTV: Intelligent Personalised TV Guides. Americal Association for Artificial Intelligence, 2000.
[8] W. Brenner, R. Zarnekow, and H. Witting. Intelligent Software Agents. Foundations and Applications, Springer-Verlag, 1998.
[9] J. L. Herlocker, J. A. Konstan, Al Borchers, and J. Riedl. An Algorithmic Framework for Performing Collaborative Filtering. In Proceedings of ACM Conference, 1999.
[10] Marko Balabanovic, and Yoak Shoham. Fab: Content-Based, Collaborative Recommendation, Communications of ACM, 1997.
[11] J. B. Schafer, J. Konstan, J. Riedl. Recommender Systems in E-Commerce. In Proceedings of ACM Conference on Electronic Commerce, pp.158-166, 1999.
[12] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Analysis of Recommendation Algorithms for E-commerce. In Proceedings of ACM Conference on Electronic Commerce, 2000.
[13] B. M. Sarwar, G. Karypis, J. A. Konstan, J. T. Riedl. Application of Dimensionality Reduction in Recommender System — A case Study. Full length paper at ACM WebKDD 2000 Web Mining for E-Commerce Workshop, 2000.
[14] Goldberg, D., Nichols, D., Oki, B. M. and Terry, D. Using Collaborative Filtering to Weave an Information Tapestry. CACM, December 1992.
[15] B. M. Sarwar, J. A. Konstan, Al Borchers, J. Herlocker, B. Miller, and J. Riedl. Using Filtering Agents to Improve Prediction Quality in the GroupLens Research Collaborative Filtering System. In Proceedings of 1998 Conference on Computer Sspported Collaborative Work, November 1998.
[16] U. Shardanand, and P. Maes. Social Information Filtering: Algorithms for Automating “Word of Mouth”.In Proceedings of the CHI ’95. Denver, CO. 1995.
[17] W. Hill, L. Stead, M. Rosenstein, G. Furnas. Recommending and Evaluating Choices in a Virtual Community of Use. Proceedings of CHI ’95, 1995.
[18] J. B. Schafer, J.A. Konstan, J. Riedl. E-Commerce Recommendaton Applications. Journal of Data Mining and Knowledge Discovery, 2001.
[19] Gheorghe Tecuci. Building Intelligent Agents: An Apprenticeship Multistrategy Learning Theory, Methodology, Tool and Case Studies. ACADEMIC PRESS. 1998.
[20] M. R. Genesereth. Pattie Maes on Software Agents:Humanizing the Global Computer. IEEE Internet Computing, July-August, pp.10-19, 1997
[21] A. Chavez and P. Maes. Kasbah: An Agent Marketplace for Buying and Selling Goods. Proceedings of International Conference on Practical Application of Intelligent Agent and MultiAgent Technology. 1996.
[22] M. R. Genesereth. And S. P. Ketchpel. Software Agent. Communication of the ACM, July, pp.48-54, 1994.
[23] J. Ross Quinlan. C4.5: Programs for Machine Learning. The Morgan Kaufmann Series in Machine Learning, Pat Langley, Series Editor, October 1992.
[24] T. Mitchell. Machine Learning. The McGraw-Hill Companies, Ine. 1996.
[25] M. Pazzahi, D. Billsus. Learning and Revising User Profiles: The Identification of interesting web sites, Machine Learning, 27, pp313-331, 1997.
[26] M. Balabanovic and Y. Shoham. Content-Based Collaborative Recommendation. Communication of the ACM, 40(3):66-72, 1997.
[27] Porter, M. F. An algorithm for suffix stripping. Program 14, 3, 130—137, 1980.
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