Adversarial web search
著者
書誌事項
Adversarial web search
(Foundations and trends in information retrieval, 4:5)
Now Publishers, c2011
大学図書館所蔵 全2件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
内容説明・目次
内容説明
Web search engines have become indispensable tools for finding content. As the popularity of the Web has increased, the efforts to exploit the Web for commercial, social, or political advantage have grown, making it harder for search engines to discriminate between truthful signals of content quality and deceptive attempts to improve search engines' rankings. This problem is further complicated by the open nature of the Web, which allows anyone to write and publish anything, and by the fact that search engines must analyze ever-growing numbers of Web pages. Moreover, increasing expectations of users, who over time rely on Web search for information needs related to more aspects of their lives, further deepen the need for search engines to develop effective counter-measures against deception.
Adversarial Web Search considers the effects of the adversarial relationship between search systems and those who wish to manipulate them, a field known as ""Adversarial Information Retrieval"". It shows that search engine spammers create false content and misleading links to lure unsuspecting visitors to pages filled with advertisements or malware. It also examines work over the past decade or so that aims to discover such spamming activities to get spam pages removed or their effect on the quality of the results reduced.
Research in Adversarial Information Retrieval has been evolving over time, and currently continues both in traditional areas (e.g., link spam) as well as newer areas, such as click fraud and spam in social media, demonstrating that this conflict is far from over.
目次
1: Introduction 2: Overview of search engine spam detection 3: Dealing with content spam and plagiarized content 4: Curbing nepotistic linking 5: Propagating trust and distrust 6: Detecting spam in usage data 7: Fighting spam in user-generated content 8: Discussion. Acknowledgements. References.
「Nielsen BookData」 より