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and Systems (NGITS), volume 2382 of Lecture Notes in Computer Science, pages 58–74, Caesarea, Israel. Springer.
[ 41 ]Cho, Junghoo; Hector Garcia-Molina (2002). "Parallel crawlers" (http:/ / portal. acm. org/ citation. cfm?id=511464). Proceedings of the 11th
international conference on World Wide Web. Honolulu, Hawaii, USA: ACM. pp. 124–135. doi:10.1145/511446.511464.
ISBN 1-58113-449-5.. Retrieved 2009-03-23.
[ 42 ]Chakrabarti, S. (2003). Mining the Web (http:/ / http://www. cs. berkeley. edu/ ~soumen/ mining-the-web/ ). Morgan Kaufmann Publishers. ISBN
1-55860-754-4
[ 43 ]Shestakov, Denis (2008). Search Interfaces on the Web: Querying and Characterizing (https:/ / oa. doria. fi/ handle/ 10024/ 38506). TUCS
Doctoral Dissertations 104, University of Turku
[ 44 ]Nelson, Michael L; Herbert Van de Sompel, Xiaoming Liu, Terry L Harrison, Nathan McFarland (2005-03-24). "mod_oai: An Apache
Module for Metadata Harvesting". Eprint arXiv:cs/0503069: 3069. arXiv:cs/0503069. Bibcode 2005cs........3069N.
[ 45 ]Making AJAX Applications Crawlable: Full Specification (http:/ / code. google. com/ web/ ajaxcrawling/ docs/ specification. html)Further reading
- Cho, Junghoo, "Web Crawling Project" (http:/ / oak. cs. ucla. edu/ ~cho/ research/ crawl. html), UCLA Computer
Science Department.
Social search
Social search or a social search engine is a type of web search that takes into account the Social Graph of the
person initiating the search query. When applied to web search this Social-Graph approach to relevance is in contrast
to established algorithmic or machine-based approaches where relevance is determined by analyzing the text of each
document or the link structure of the documents.[1] Search results produced by social search engine give more
visibility to content created or touched by users in the Social Graph.
Social search takes many forms, ranging from simple shared bookmarks or tagging of content with descriptive labels
to more sophisticated approaches that combine human intelligence with computer algorithms.[2][3]
The search experience takes into account varying sources of metadata, such as collaborative discovery of web pages,
tags, social ranking, commenting on bookmarks, news, images, videos, knowledge sharing, podcasts and other web
pages. Example forms of user input include social bookmarking or direct interaction with the search results such as
promoting or demoting results the user feels are more or less relevant to their query.[4]History
The term social search began to emerge between 2004 and 2005. The concept of social ranking can be considered to
derive from Google's PageRank algorithm, which assigns importance to web pages based on analysis of the link
structure of the web, because PageRank is relying on the collective judgment of webmasters linking to other content
on the web. Links, in essence, are positive votes by the webmaster community for their favorite sites.
In 2008, there were a few startup companies that focused on ranking search results according to one's social graph on
social networks.[5][6] Companies in the social search space include Wajam, folkd, Slangwho, Sproose, Mahalo,
Jumper 2.0, Qitera, Scour, Wink, Eurekster, Baynote, Delver, OneRiot, and SideStripe. Former efforts include Wikia
Search. In 2008, a story on TechCrunch showed Google potentially adding in a voting mechanism to search results
similar to Digg's methodology.[7] This suggests growing interest in how social groups can influence and potentially
enhance the ability of algorithms to find meaningful data for end users. There are also other services like Sentimnt
that turn search personal by searching within the users' social circles.
The term 'Lazyweb' has been used to describe the act of out-sourcing your questions to your friends, usually by
broadcasting them on Twitter or Facebook (as opposed to posting them on Q&A websites such as Yahoo Answers).
The company Aardvark, acquired by Google in February 2010, has created a more targeted version of this, which
directs your questions to people in your social networks, based on relating the content of the question to the content
of their social network pages. Aardvark users primarily use the Aardvark IM buddy, also integrated into Google