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Social search is a behavior of retrieving and searching on a social searching engine that mainly searches user-generated content such as news, videos and images related search queries on social media like Facebook, Twitter, Instagram and flickr.〔(socialseeking about, Copyright 2014 VSmaster.com. Retrieved December 1st, 2015 )〕 Social search or a social search engine is an enhanced version of web search that combines traditional algorithm.The idea behind social search is that instead of a machine deciding which pages should be returned for a specific query based upon an impersonal algorithm, results that are based on the human network of the searcher might be more relevant to that specific user's needs.〔(Definition - What does Social Search mean?. Retrieved December 1st, 2015 )〕〔(DEFINITION social search engine, Margaret Rouse, November 2011. Retrieved December 1st, 2015 )〕 Social search may not be demonstrably better than algorithm-driven search. In the algorithmic ranking model that search engines used in the past, relevance of a site is determined after analyzing the text and content on the page and link structure of the document. In contrast, search results with social search highlight content that was created or touched by other users who are in the Social Graph of the person conducting a search. It is a personalized search technology with online community filtering to produce highly personalized results. 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. Depending on the feature-set of a particular search engine, these results may then be saved and added to community search results, further improving the relevance of results for future searches of that keyword. The principle behind social search is that instead of computer algorithms deciding the results for specific queries, human network oriented results would be more meaningful and relevant for the user. 〔(Chi, Ed H. Information Seeking Can Be Social, Computer, vol. 42, no. 3, pp. 42-46, Mar. 2009, ) 〕〔(A Taxonomy of Social Search Approaches ), Delver company blog, Jul 31, 2008〕〔(Longo, Luca et al., Enhancing Social Search: A Computational Collective Intelligence Model of Behavioural Traits, Trust and Time. Transactions on Computational Collective Intelligence II, Lecture Notes in Computer Science, Volume 6450. ISBN 978-3-642-17154-3. Springer Berlin Heidelberg, 2010, p. 46 ) 〕〔(Longo, Luca et al., Information Foraging Theory as a Form of Collective Intelligence for Social Search. Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems Lecture Notes in Computer Science, 2009, Volume 5796/2009, 63-74 ) 〕 Benefits of social search: * The retrieved results would be more relevant to the user and the needs because the results are culled from the content streams of human beings in your social groups. * It can help in building a trusted network because social search provides a way to leverage a network of trusted people, relying more upon their own impression about a particular result being good or bad. * Since the results are products of human involvement, it can be more helpful and relevant and would also help in bettering computer algorithms to suit different human networks. * Negligible spamming occurs through social search, as it is more based on personal feedback. * Social search also provides results which are current and up to data with even recent changes because there is a constant feedback loop involved. Negatives of social search: * Users directly add results to a social engine. Therefore, without proper control, users can be abused by the results with search spam. * The long search terms are not very suited for social search due to low possibility to fill in all the searches with content users provide or fill in. 〔(What Is Social Search?, Trond Lyngbø on January 18, 2013. Retrieved December 1st, 2015 )〕 ==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.〔(New Sites Make It Easier To Spy on Your Friends ), Wall Street Journal, May 13. 2008〕〔(Social Search Guide: 40+ Social Search Engines ), Mashable, Aug 27. 2007〕 Companies in the social search space include Evam-SOCOTO Wajam, Slangwho, Sproose, Mahalo, Jumper 2.0, Qitera, Scour, Wink, Eurekster, Baynote, Delver, and OneRiot. 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.〔(Is This The Future Of Search? ), TechCrunch, July 16, 2008〕 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 Sentiment that turn search personal by searching within the users' social circles. In October 2009, Google rolled out its "Social Search" feature; after a time in beta, the feature was expanded to multiple languages in May 2011. Before the expansion however in 2010 Bing and Google were already taking into account re-tweets and Likes when providing search results. However, after a search deal with Twitter ended without renewal, Google began to retool its Social Search. In January 2012, Google released "Search plus Your World", a further development of Social Search. The feature, which is integrated into Google's regular search as an opt-out feature, pulls references to results from Google+ profiles. The goal was to deliver better, more relevant and personalized search results with this integration. This integration however had some problems in which Google+ still isn't wildly adopted or has much usage among many users. In January 2013, Facebook announced a new search engine called Graph Search still in the beta stages. The goal in mind was to accomplish what Google failed at, skipping the results that are popular to the internet, in favor of the results that are popular within your social circle. Unlike Google, Facebook's Graph search differed in two large areas, first, people use Facebook frequently. This allows Facebook to use all its user generated content that is uploaded everyday to improve the Facebook search experience.〔 Secondly, Facebook did not incorporate Google into Facebook search, instead Graph Search is powered by Bing. This allows Bing results to show when Facebook's Graph Search can't find a match. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「social search」の詳細全文を読む スポンサード リンク
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