Social search
From Wikipedia, the free encyclopedia
A social search engine is a type of search engine that determines the relevance of search results by considering the interactions or contributions of users. 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. 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 (ex: the basis of PageRank). 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.
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[edit] History
The term social search began to emerge between 2004 and 2005. On January 21, 2004 Eurekster was referred to as personalized social search [1]. On June 28, 2005 Jeremy Zawodny at Yahoo referred to the Yahoo! MyWeb product as 'social search'. On October 26, 2005 Pete Cashmore applied the term 'social search' to Wink.
And despite the current awareness, the concept of social search 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 judgement of webmasters linking to other content on the web. Links, in essence, are positive votes by the webmaster community for their favorite sites.
Social search as it's evolving today incorporates both automated software as well as human judgments about the nature of web content.
Social search is information retrieval, wayfinding tools informed by human judgment. It's about people helping people find stuff. Social search experiences revolve around the outcome of collaborative harvesting, directory building, tagging, social ranking, question & answers services, shared bookmarks and Web pages. Social Search is surfacing the tail and already channeling significant amounts of traffic around. Information retrieval is reaching another inflexion point. There is a shift taking place from search engines having the power to search users getting empowered, from the head to the tail, from a "few-to-many" to a "many-to-many" publishing model. Social Search is changing the rules, shifting power to the people. Another way to think of it is Social Search is the 3rd big evolution of the search business after i) algorithmic search, ii) paid search models, and now iii) Social Search. Web 2.0 trends converge toward social search: social networking, consumer generated media, open platforms and syndication models, new user interaction models. To paraphrase Microsoft's Ramez Naam, it's like every human being is a neuron, and humanity as a whole is one giant brain, smarter as a connected whole. If you can increase the ability of humans to communicate with each other, you make the whole planet smarter.
As articulated by Chris Sherman, social search is information retrieval, way finding tools informed by human judgment. Social search is people helping people find stuff using plain-language questions and answers, collaborative content harvesting, directory building, voting and ranking, sharing, tagging, commenting on bookmarks, web pages, news, images, videos and podcasts.
[edit] 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
[edit] Concerns
- Risk of spam. Because users can directly add results to a social search engine there is a risk that some users could insert search spam directly into the search engine. Elimination or prevention of this spam would require the ability to detect the validity of a users' contribution, such as whether it agrees with other trusted users.
- "The Long Tail" of search is a concept that there are so many unique searches conducted that most searches, while valid, are performed very infrequently. A search engine that relied on users filling in all the searches would be at a disadvantage to one that used machines to crawl and index the entire web.
[edit] See also
[edit] References
- ^ Sullivan, Danny. Eurekster Launches Personalized Social Search. Search Engine Watch. January 21, 2004.
