  Email Retrieval task has recently taken much attention to help the user
retrieve the email(s) related to the submitted query. Up to our knowledge,
existing email retrieval ranking approaches sort the retrieved emails based on
some heuristic rules, which are either search clues or some predefined user
criteria rooted in email fields. Unfortunately, the user usually does not know
the effective rule that acquires best ranking related to his query. This paper
presents a new email retrieval ranking approach to tackle this problem. It
ranks the retrieved emails based on a scoring function that depends on crucial
email fields, namely subject, content, and sender. The paper also proposes an
architecture to allow every user in a network/group of users to be able, if
permissible, to know the most important network senders who are interested in
his submitted query words. The experimental evaluation on Enron corpus prove
that our approach outperforms known email retrieval ranking approaches
