  Email has remained a principal form of communication among people, both in
enterprise and social settings. With a deluge of emails crowding our mailboxes
daily, there is a dire need of smart email systems that can recover important
emails and make personalized recommendations. In this work, we study the
problem of predicting user triage actions to incoming emails where we take the
reply prediction as a working example. Different from existing methods, we
formulate the triage action prediction as a recommendation problem and focus on
the content-based approach, where the users are represented using the content
of current and past emails. We also introduce additional similarity features to
further explore the affinities between users and emails. Experiments on the
publicly available Avocado email collection demonstrate the advantages of our
proposed recommendation framework and our method is able to achieve better
performance compared to the state-of-the-art deep recommendation methods. More
importantly, we provide valuable insight into the effectiveness of different
textual and user representations and show that traditional bag-of-words
approaches, with the help from the similarity features, compete favorably with
the more advanced neural embedding methods.
