  With the rise of social networks, information on the internet is no longer
solely organized by web pages. Rather, content is generated and shared among
users and organized around their social relations on social networks. This
presents new challenges to information retrieval systems. On a social network
search system, the generation of result sets not only needs to consider keyword
matches, like a traditional web search engine does, but it also needs to take
into account the searcher's social connections and the content's visibility
settings. Besides, search ranking should be able to handle both textual
relevance and the rich social interaction signals from the social network. In
this paper, we present our solution to these two challenges by first
introducing a social retrieval mechanism, and then investigate novel deep
neural networks for the ranking problem. The retrieval system treats social
connections as indexing terms, and generates meaningful results sets by biasing
towards close social connections in a constrained optimization fashion. The
result set is then ranked by a deep neural network that handles textual and
social relevance in a two-tower approach, in which personalization and textual
relevance are addressed jointly. The retrieval mechanism is deployed on
Facebook and is helping billions of users finding postings from their
connections efficiently. Based on the postings being retrieved, we evaluate our
two-tower neutral network, and examine the importance of personalization and
textual signals in the ranking problem.
