This document discusses social search engines, which take a user's social graph into account when returning search results. It provides three types of social searches: collective social search, which leverages trends from a network; friend-filtered social search, which shows what a user's friends have shared; and collaborative search, where users work together to find answers. The document also outlines benefits of social search like increased relevance and current results, and concludes that social search complements collaborative information seeking by allowing temporary collaboration during individual searches.

1. Submitted by:-
Manu Kumar K M
IInd MLISc
DOS in LIS
Manasagangotri, Mysore.

2. 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 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.
The social search concept took off a year or two after
Facebook popularized online social networking. A bunch of
startups launched Web search services that ranked results
based on what people were searching or clicking on within a
particular circle of people--user of a specific website, for
example, or a social network like Facebook.

3. 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
Page Rank algorithm,which assigns importance to
web pages based on analysis of the link structure
of the web, because Page Rank 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.

4. Types of Social searches.
Collective Social Search
"Collective social search" is similar in concept to the wisdom of
crowds, in that search is augmented by trends shared on a network or
results ranked against the real-time buzz of a group. Why might this be
useful? Well, in some instances, we can't immediately find the
information we're looking for; and pooled data from the collective may
point us to new avenues that expand our discovery process.
Friend-Filtered Social Search
Friend-filtered social search is approximately what Google is
doing with its social search experiment: providing social data that your
Friends, friends of friends and wider "social circle" have shared. This
data could appear alongside traditional search results or be exclusive
results from within your peer network.
Collaborative Search
"Collaborative search" is when two or more users work together
to find the answer to a problem. This could look like IM-based question-
answering Yahoo! Answers or over-the-shoulder two-person search. In all
of these cases, people speak to each other using natural language, which
is incredibly useful for open-ended queries or queries about unfamiliar
domains. Such conversations, even not real-time ones, can assist people
who don't know the right keywords to use.

5. Benefits
Increased relevance because each result has been
selected by users.
Reduced impact of link spam by relying less on link
structure of web pages.
Leverage a network of trusted individuals by
providing an indication of whether they thought a
particular result was good or bad.
Shearing the Information and Resource.
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.

6. Conclusion
Social search intended to document the
ways in which social interactions play a role in
search tasks online. We believe that it
complements related work in online collaborative
information seeking that has examined active
cooperation during co-located, synchronous
searches. Although most of our users were not
explicitly collaborating with others in joint search
tasks, we have shown how people can be
momentarily recruited to collaborate during
certain phases of search to help with
individual, but otherwise non-
collaborative, search goals.