[6] 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.
[7] 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.
[12][13] Companies in the social search space include Sproose, Mahalo, Jumper 2.0, Scour, Wink, Eurekster, and Delver.
In 2008, a story on TechCrunch showed Google potentially adding in a voting mechanism to search results similar to Digg's methodology.
[14] This suggests growing interest in how social groups can influence and potentially enhance the ability of algorithms to find meaningful data for end users.
In October 2009, Google rolled out its "Social Search"; after a time in beta, the feature was expanded to multiple languages in May 2011.
One famous example occurred when Google showed a link to Mark Zuckerberg's dormant Google+ account rather than the active Facebook profile.
[22] In the end though social search will never be truly comprehensive of the subjects that matter to people unless users opt to be completely public with their information.
Social search engines are considered a part of Web 2.0 because they use the collective filtering of online communities to elevate particularly interesting or relevant content using tagging.
These descriptive tags add to the meta data embedded in Web pages, theoretically improving the results for particular keywords over time.
As these are trust-based networks, unintentional or malicious misuse of tags in this context can lead to imprecise search results.
[31] Other versions of social engines have been launched, including Google Coop, Eurekster, Sproose, Rollyo, Anoox and Yahoo's MyWeb2.0.
Confirmed to be in testing, a new Facebook app feature called 'Add a Link' lets users see popular articles they might want to include in their status updates and comments by entering a search query.
The option certainly makes it easier for users to add links without manually searching their News Feed or resorting to a Google query.
[35] However this is not possible unless social media sites decide to work with search engines, which is difficult since everyone would like to be the main toll bridge to the internet.
As we continue on, and more articles are referred by social media sites, the main concern becomes what good is a search engine without the data of users.
The infrastructure required for a search engine is already available in the form of thousands of idle desktops and extensive residential broadband access.
[37][38] Another issue related to both distributed and centralized search is how to more accurately understand user intent from observed multimedia data.
[39][40] Besides above engineering explorations, a more fundamental and potential method is to develop social search systems based on the understanding of related neural mechanisms.
For search scenarios, organisms must detect – and climb – noisy, long-range environmental (e.g., temperature, salinity, resource) gradients.
Here, social interactions can provide substantial additional benefit by allowing individuals, simply through grouping, to average their imperfect estimates of temporal and spatial cues (the so-called ‘wisdom-of-crowds’ effect).