题名

阻尼因子對網頁排名之敏感度分析

并列篇名

Sensitivity Analysis on Damping Factor in Google PageRank

DOI

10.29973/JCSA.200506.0002

作者

傳懷慧(Hwai-Hui Fu)；林共進(Dennis K. J. Lin)；白峰杉(Feng-Shan Bai)；蔡憲唐(Hsien-Tang Tsai)；韋伯韜(Duan Wei)

关键词

搜尋引擎 ； 索引分析 ； 馬可夫鏈 ； Citation analysis ； markov chain ； search engine

期刊名称

中國統計學報

卷期/出版年月

43卷2期（2005 / 06 / 01）

页次

145 - 164

内容语文

繁體中文
中文摘要

全球第一大搜尋引擎Google以PageRank技術來決定網頁排名。因此，深入瞭解PageRank的基本原理就更顯得有其必要性。本文利用所有基本典型範例來探討公式中阻尼因子(damping factor)變化對PageRank值的影響趨勢。本研究結果顯示：在不同網頁超連結方式，阻尼因子變動確實會對超連結內個別網頁PageRank值產生不同程度的影響，甚至於影響網頁排名，此技術即所謂索引分析(Citation analysis)。進一步研究發現：馬可夫鏈(Markov chain)理論恰好能解釋網頁搜尋特性，並推導得知阻尼因子靠近1較好，但是又不能過分靠近1，此與Google取阻尼因子值為0.85狀況不謀而合。
英文摘要

Google-the largest search engine in the world-uses PageRank technology to determine the rank of website listings. It is thus important to have a decent understanding on PageRank technology for a fair ranking system. In this paper, we study the sensitivity on damping factor in PageRank, by investigating all typical linkage examples in the article ”The Google Page Rank Algorithm and How It Works” (Roger, 2002). It is shown that the choice of the value for damping factor is critical to the PageRank. The degree of impact varies from one case to another. This ranking technology is called Citation analysis. Furthermore, Markov chain theory can be used here for modeling webpage's characteristics. The applications of Markov chain indicates that damping factor should be closer, but not equal to 1. These results support the choice of damping factor d=0.85 currently used by Google.
主题分类 基礎與應用科學 > 統計
参考文献
  1. Brin, S.,Page, L.(1998).The anatomy of a large-scale hypertextual web search engine.Computer Networks and ISDN Systems,30,107-117.
  2. The Second Eigenvalue of the Google Matrix
  3. Mangalindan, M.(2003).Seeking growth, search engine google acts like ad agency.Wall Street Journal,16
  4. Mcuhan, R.(2000).Search for a top ranking.Marketing,19,47.
  5. Meghabghab, G.(2001).Google`s web page ranking applied to different topological web graph structures.Journal of the American Society for Information Science and Technology,52,736-747.
  6. Notess, G. R.(1999).Rising relevance in search engines.Online,23,84-86.
  7. Page, L.,Brin, S.,Motwani, R.,Winograd, T.(1998).The PageRank citation ranking: Bringing order to the web.Stanford Digital Library Technologies Project.
  8. The Google Pagerank Algorithm and How It Works
  9. Ward, E.(2000).Market through `Link Analysis` to improve popularity quality.Advertising Age`s Business Marketing,85,32-33.
  10. Weidlich, T.(2002).Search engine marketing revving up.Catalog Age,19,3-6.
  11. 搜尋引擎之最優排序：回顧綜?、管理意涵與研究展望
print cancel