Reply With: Proactive Recommendation of Email Attachments

TL;DR

This paper presents a weakly supervised learning framework for proactively recommending email attachments by formulating effective search queries from conversation context, reducing user effort in email responses.

Contribution

It introduces a novel weakly supervised label generation strategy and a deep CNN model for query formulation in email attachment recommendation.

Findings

The approach achieves satisfactory performance on Avocado and proprietary datasets.

Proactive recommendations can significantly reduce email response time.

The framework leverages existing IR systems without manual annotation.

Abstract

Email responses often contain items-such as a file or a hyperlink to an external document-that are attached to or included inline in the body of the message. Analysis of an enterprise email corpus reveals that 35% of the time when users include these items as part of their response, the attachable item is already present in their inbox or sent folder. A modern email client can proactively retrieve relevant attachable items from the user's past emails based on the context of the current conversation, and recommend them for inclusion, to reduce the time and effort involved in composing the response. In this paper, we propose a weakly supervised learning framework for recommending attachable items to the user. As email search systems are commonly available, we constrain the recommendation task to formulating effective search queries from the context of the conversations. The query is submitted to an existing IR system to retrieve relevant items for attachment. We also present a novel strategy for generating labels from an email corpus---without the need for manual annotations---that can be used to train and evaluate the query formulation model. In addition, we describe a deep convolutional neural network that demonstrates satisfactory performance on this query formulation task when evaluated on the publicly available Avocado dataset and a proprietary dataset of internal emails obtained through an employee participation program.