A New Email Retrieval Ranking Approach

TL;DR

This paper introduces a novel email retrieval ranking method that uses a scoring function based on email fields and an architecture for identifying key network senders, outperforming existing approaches on the Enron dataset.

Contribution

It proposes a new ranking approach based on email content and sender importance, and an architecture for network sender identification, enhancing retrieval effectiveness.

Findings

Outperforms existing email ranking methods on Enron corpus.

Utilizes a scoring function based on subject, content, and sender.

Provides an architecture for identifying important network senders.

Abstract

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.