Social search is a type of web-search technique that infers the relevance of web-search results by considering the opinions of end-users as to the value of web content.

Recently, the concept of social search has been acquiring importance in the world wide web as large-scale collaborative computing environments have become feasible. The advantage of systems supporting social search is that Web pages are considered relevant and trustworthy from the reader's perspective rather than authors' perspective. This approach takes many forms, from the simplest, based on sharing bookmarks or tagging of content with descriptive labels, to more sophisticated approaches that combine human intelligence with computer paradigms supporting collaborative gathering, collaborative directories, tags and commenting on bookmarks, news, videos images and other web pages.

Social Search contrasts with the majority of the current search engines, above all Google, whose PageRank algorithm assigns importance to web pages based on analysis of the link structure of the web in order to find the most authoritative pages.

A key open challenge in designing social search systems is to improve the overall information seeking and consuming activities on the web.