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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. 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. 
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.
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. 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. 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. 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 company was subsequently criticized by Twitter for the perceived potential impact of "Search plus Your World" upon web publishers, describing the feature's release to the public as a "bad day for the web", while Google replied that Twitter refused to allow deep search crawling by Google of Twitter's content.
Social discovery is the use of social preferences to predict what content will be desirable to the user. Technology is used to discover new people and sometimes new experiences shopping, meeting friends or even traveling.  The discovery of new people is often in real-time, enabled by mobile apps. However, social discovery is not limited to meeting people in real-time, it also leads to sales and revenue for companies via social media.  An example of retail would be the addition of social sharing with music, through the iTunes music store. There is a social component to discovering new music  Social discovery is at the basis of Facebook's profitability, generating ad revenue by targeting the ads to users using the social connections to enhance the commercial appeal.
- Collaborative filtering
- Enterprise bookmarking
- Human search engine
- Relevance feedback
- Social software
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