(Gautam Das, Sushruth, Lekhendro Singh)

We describe a social search engine paradigm which can be built on top of a classic search engine (e.g. Google, Yahoo, etc.) and a social information network (such as FriendSter). Our objective was to design algorithms and develop methods to efficiently combine information available in the underlying systems (Search Engine & Social Information Network) to better satisfy the search needs of a user. We are interested on how to efficiently employ social information to re-order a list of URLs retrieved by querying a search engine. The objective was to re-order the list of URLs in a way that favors URLs that are more relevant to users interest towards a personalized search engine. We show through rigorous experimental results that the probability of search results obtained using this method are superior compared to the results of the regular search engines.