Social search or a social search engine is a type of web search method that determines the relevance of search results by considering the interactions or contributions of users. When applied to web search this user-based 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.

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, 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.

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,fact: date=March 2009 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 are a few startup companies that focus on ranking search results according to one's social graph on social networks. Companies in the social search space include Mahalo, Stumpedia, Wikia Search, Qitera, Scour, Wink, Eurekster, Baynote, Delver, OneRiot and SideStripe. 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.

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 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 to ask and answer their questions.

Benefits

To date social search engines have not demonstrated measurably improved search results over algorithmic search engines. However, there are potential benefits deriving from the human input qualities of social search.

• Reduced impact of link spam by relying less on link structure of web pages.
• Increased relevance because each result has been selected by users.
• Leverage a network of trusted individuals by providing an indication of whether they thought a particular result was good or bad.
• The introduction of 'human judgement' suggests that each web page has been viewed and endorsed by one or more people, and they have concluded it is relevant and worthy of being shared with others using human techniques that go beyond the computer's current ability to analyze a web page.
• Web pages are considered to be relevant from the reader's perspective, rather than the author who desires their content to be viewed, or the web master as they create links.
• More current results. Because a social search engine is constantly getting feedback it is potentially able to display results that are more current or in context with changing information.