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系統識別號 U0002-1706200517331100
中文論文名稱 設計網路文章推薦系統於電腦輔助英語教學
英文論文名稱 The Designing of a Document Recommendation System for Computer Assisted English Learning on the Web
校院名稱 淡江大學
系所名稱(中) 資訊工程學系碩士班
系所名稱(英) Department of Computer Science and Information Engineering
學年度 93
學期 2
出版年 94
研究生中文姓名 鄧力維
研究生英文姓名 Li-Wei Teng
學號 692191421
學位類別 碩士
語文別 中文
口試日期 2005-06-16
論文頁數 80頁
口試委員 指導教授-郭經華
委員-陳孟彰
委員-劉遠楨
委員-郭經華
中文關鍵字 以英文為第二外語  文章評分過濾器  字彙曝光率 
英文關鍵字 English as Second Language  Language Difficulty Filter  word repeated exposure 
學科別分類 學科別應用科學資訊工程
中文摘要 在本論文中,我們提供了一個網路上的文章推薦系統給第二語言學習者當作學習工具。事實上我們可以說這一個系統是一個具有適性化的網路文章推薦系統,應用在給予以英文為第二外語的學習者。

首先我們會提供相似網頁給予使用者,以提高字彙曝光率,以增竟學習效能,我們亦會記錄使用者資訊,以當作使用者等級讓接著的文章評分過濾器,能夠照著使用者的等級加以分析推薦適合使用者的文章,而過濾掉與使用者程度不符的文章,本論文最主要的貢獻便在於利用全球資訊網之便利性,將學習置於網路上增加學習之便利、提供使用者搜尋相似網頁,提高字彙之曝光率提升學習效率、透過調整適當之門檻值讓文章評分過濾器更具適性化,推薦之文章更能貼近使用者程度。

在實作本系統時,我們將調整文章評分過濾器中所用到的門檻值,來達到適性化。我們也利用實驗數據來證明我們所訂定的門檻值是否能符合我們的系統,我們將六等級及資料庫中的資料,調整不同的門檻值來檢視六等級之分佈是否在資料庫中,在將之轉換成高斯分佈,看看兩者高斯分佈是否貼近,並以數據量化高斯分佈圖形,以數據檢視兩分佈是否接近,月接近表示兩者難易分佈是一樣的,而2000這個門檻值也的確是可以符合我們的系統的,這也是本論文之貢獻之一。
英文摘要 In this paper, we propose a Document Recommendation System on WWW for English as Second Language(ESL) learners. Actually we can say the system is a personal recommendation system for ESL learners.

First, we provide the similar pages for those learners, and the similar pages that we provide can be regarded as the same theme which the pages discussed. We also record the degree of learners and use a Language Difficulty Filter(LDF) to filter out the pages which is not accord with the learner’s degree as our main component in the system. The main idea of our system is to raise the rate of repeated exposure of the word which user wants to know. So we provide this system, in addition to raise the rate of repeated exposure of words, we also choose the pages which accord with the learner’s degree.

To test the actually system, we adjust the threshold for our system. With this system, we will build the Gaussian Distribution for both scores of six degree and data in our data base and then we will examine the Chi-Square test statistics from the distribution of them for the different threshold of LDF subsystem. After examining and analyzing the results, we concluded through expand by sense , the threshold (2000) of the LDF subsystem as a whole has a dramatic improvement of personally recommend. Beside the data in our data base, we can also use the keywords with a closer definition with the image we desire.
論文目次 目錄
第1章 緒論...................................3
1.1 研究動機與目的............................3
1.2 研究內容..................................6
1.3 論文內容大綱..............................7

第2章 背景知識與相關研究..................8
2.1 搜尋引擎系統之發展過程....................9
2.2 網頁蒐集機制.............................11
2.3 以內容為基礎之相似網頁搜尋技術...........15
2.4 以連結為基礎之相似網頁搜尋技術...........18
2.5 英國國家標準語料庫.......................24
2.6 詞性標記.................................28
2.7 開放式目錄結構計畫.......................31

第3章 系統架構.............................34
3.1 系統架構.................................34
3.2 相似網頁索引系統.........................38
3.2.1 網頁蒐集...............................39
3.2.2 相似網頁文章分群.........................40
3.2.3 相似網頁文章索引.........................42
3.3 文章評分推薦系統.........................44
3.3.1 字及詞性評分.............................45
3.3.2 文章評分................................48
3.3.3 使用者資訊............................59
3.4 網路文章關連性資料庫.....................61

第4章 實作與討論...........................63
4.1 實作介面.................................63
4.2 文章推薦系統的效能評估...................66

第5章 結論與未來研究......................74
5.1 結論.....................................74
5.2 未來研究方向.............................76

參考文獻......................................78
參考文獻 參考文獻:
[1] S. M. Shieh, Personal Documents Recommendation System Based on Data Mining Techniques
[2] Dragomir R. Radevyz, WebInEssence: A Personalized Web-Based Multi-Document Summarization and Recommendation System
[3] Google Inc. http://www.google.com.
[4] OpenFind http://www.openfind.com.tw.
[5] YAHOO http://www.yahoo.com.tw.
[6] A. Heydon and M. Najork. Mercator: A scalable,extensible web crawler. Word Wide Web, 2(4):219–229, December 1999.
[7] L. Page and S. Brin. The anatomy of a large-scale hypertextual web search engine. In Proc. of WWW Conf., 1998.
[8] S. Chakrabarti, M. van den Berg, and B. Dom. Focused crawling: A new approach to topic-specific web resource discovery. In Proc. of WWW Conf., 1999.
[9] J. Cho and H. Garcia-Molina. Synchronizing a database to improve freshness. In Proc. of SIGMOD Conf., 2000.
[10] M. T. Ozsu and P. Valduriez. Principles of Distributed Database Systems. Prentice Hall, 1999.
[11] A. S. Tanenbaum and R. V. Renesse. Distributed operating systems. ACM Computing Surveys, 17(4), December 1985.
[12]Weblech URL Spider
http://weblech.sourceforge.net/
[13] G. Salton and M. J. McGill,“Introduction to Modern Information Retrieval”, McGraw-Hill Book Co., New York, 1983.
[14] R. Baeza-Yates and B. Ribeiro-Neto,“Moderm Information Retrieval”, Addison Wesley Longman, Inc, May 1999.
[15] ZHANG, T., RAMAKRISHNAN, R., AND LIVNY, M. 1996. BIRCH: An efficient data clustering method for very large databases. SIGMOD Rec. 25, 2, 103–114.
[16] DEAN, J. AND HENZINGER, M. R. 1999. Finding related pages in the world wide web. In Proceedings of the Eighth International Conference on The World-Wide Web.
[17] K. Bharat and M. Henzinger, Improved algorithms for topic distillation in hyperlinked environments, in: Proc. of the 21st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’98), pp.104–111, 1998.
[18] J. Kleinberg, Authoritative sources in a hyperlinked environment, in: Proc. of the 9th Annual ACM–SIAM Symposium on Discrete Algorithms, pp. 668–677, January 1998.
[19] Taher H. Haveliwala, Evaluating Strategies for Similarity Search on the Web
[20]BNC - British National Corpus.
[21] Thorsten.Brants, TnT-A Statistical Part-of-Speech Tagger. In Proceedings of the Sixth Applied Natrual Language Processing Conference ANLP-2000, Seatle,WA, 2000.
[22] http://www.coli.uni-sb.de/sfb378/negra-corpus/
[23] http://www.cogs.susx.ac.uk/users/geoffs/RSue.html
[24] Open Directory Project (ODP). http://www.dmoz.com/.
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