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研究生:鄭國宏
研究生(外文):Kuo-Hung Cheng
論文名稱:在搜尋引擎內利用連結關係完成使用者設定描述
論文名稱(外文):User Profiling by Link Relationship in a Search Engine
指導教授:鍾崇斌
指導教授(外文):Chung-Ping Chung
學位類別:碩士
校院名稱:國立交通大學
系所名稱:資訊工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:英文
中文關鍵詞:連結關係使用者設定加權值連結值
外文關鍵詞:link relationshipuser profilingauthority valuehub value
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隨著網際網路的資訊越來越豐富,使用者必須花更多時間來尋找其所需要的資料。搜尋引擎是個在網際網路中擷取資訊很有用的工具,它根據使用者所查詢的字來做回應。但是在真實狀況下,對於相同的查詢字而言,每一位使用者所想要的結果是不同的。
本篇論文的目的,是要對網路上查詢的結果做個人化。我們提出一個利用連結關係來做使用者設定的方法。和向量空間模式不同的是,我們所提出的方法不依靠文字而只依靠在網際網路中網頁上的連結關係。利用這個連結的關係來衡量網頁使用者設定的加權值與連結值,並用這些值來做網頁的排序,而且事先預測使用者喜愛的網頁。簡而言之,如果一個網頁被很多其他網頁所連結,我們則認為這個網頁是有價值的。進一步來說,如果一個網頁被很多同一使用者曾經選過的網頁所連結,我們則認為這個網頁也應該是此使用者所感興趣的。在我們的模擬中,利用現在網路中實際的網頁連結關係與各種不同的預測使用者興趣來評估預測的準確率,此結果似乎是可被證實的。

As information in World Wide Web becomes richer and richer, the users have to spend more and more time finding what they want. The search engine is one of the useful tools to retrieve information in WWW and it replies to users according to the query terms. But the real status is that user wanted results of the same query term are different respectively.
The objective of the thesis is to personalize the web query result. We proposed a method by using link relationship to do the personalization. Differing from the vector-space model, the proposed method depends not on terms but only on link relationship between pages in WWW. The link relationship would be used to evaluate the personal authority value and hub value. These values are also used to rank pages and speculate the user favorite pages in advance. In brief, if many other pages link a page, we think this page should be valuable. Furthermore, if a particular page is linked by many others pages which the particular user chosen before, we think this page should also be interesting to the user. In our simulation, we use the link relationship between pages in World Wide Web and different user’s interests to evaluate the speculation. The results seem to be promising.

Contents
摘要 I
ABSTRACT II
誌謝 III
CONTENTS IV
LIST OF FIGURES VI
CHAPTER 1 INTRODUCTION 1
1.1 OBSERVATIONS AND MOTIVATIONS 1
1.2 PROPOSED SYSTEM WITH A SEARCH ENGINE 2
1.3 INTRODUCTION TO INFORMATION FILTERING SYSTEM 3
1.4 PREVIOUS WORK OF INFORMATION FILTERING SYSTEM 5
1.5 ORGANIZATION OF THE THESIS 5
CHAPTER 2 RELATED WORK 6
2.1 INTRODUCTION OF WEB PERSONALIZATION 6
2.2 INFORMATION FILTERING SYSTEM UNDER VECTOR-SPACE MODEL 7
2.2.1 Introduction to Vector-Space Model 7
2.2.2 User Profiling Under Vector-Space Model 8
2.2.3 Observed Characteristics of Vector-Space Model 9
2.3 HYPERLINK AND LINK CONNECTIVITY 10
2.4 SUMMARY OF RELATED WORK 12
CHAPTER 3 PROPOSED USER PROFILING 14
3.1 OVERVIEW OF PROPOSED INFORMATION FILTERING SYSTEM 14
3.2 PERSONALIZED RANKING 16
3.2.1 Principles of Personalized Ranking Concepts 17
3.2.2 User Profile Structure 17
3.2.3 Weighting Method of On-Line Ranking 19
3.3 FEEDBACK 21
3.3.1 Update 22
3.3.2 Ripple 24
3.4 SUMMARY 29
CHAPTER 4 EVALUATION AND DISCUSSION 30
4.1 OBJECTIVE AND METRIC OF EVALUATION 30
4.2 FLOWCHART OF EVALUATION 31
4.3 TEST DATA 34
4.4 RESULTS 36
4.5 DISCUSSION 40
CHAPTER 5 CONCLUSIONS 43
REFERENCE 45

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[2] Keiichiro Hoashi, Kazunori Matsumoto, Naomi Inoue, Kazuo Hashimoto. 1999. Query expansion method on word contribution. ACM SIGIR
[3] Keiichiro Hoashi, Kazunori Matsumoto, Naomi Inoue, Kazuo Hashimoto. 2000. Document filtering method using non-relevant information profile. ACM SIGIR
[4] J. Mostafa, S. Mukhopadhyay, W. Lam and M. Palakal. 1997. A multilevel approach to intelligent information filtering: model, system, and evaluation. ACM Transaction on information systems
[5] Jon M. Kleinberg. September 1999. Authoritative sources in a hyperlinked environment. Journal of the ACM, pp. 604-632
[6] Ricardo Baeza-Yates, Berthier Ribeiro-Neto. 1999. “Modern Information Retrieval”
[7] Soumen Chakrabarti, Jon M. Kleinberg. 1998. Automatic Resource Compilation by Analyzing Hyperlink Structure and Associated Text.
[8] http://tw.yahoo.com
[9] Ricardo Baeza-Yates, William Frakes. 1999. “Information Retrieval: Data Structure and Algorithm”
[10] http://www.google.com
[11] http://www.teoma.com
[12] Krishna Bharat, Monika R.Henzinger. 1998. “Improved algorithms for topic distillation in a hyperlinked environment” 21st ACM SIGIR.
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[14] Jeffrey Dean, Monika R. henzinger. 1999. “Finding related pages in the world wide web” Comapq Western Research Laboratory.
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[17] Seon-Mi Woo, Chun-Sik Yoo, Yong-Sung Kim. 1999. “User-centered filtering and document ranking” IEEE TENCON

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