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      Reply With: Proactive Recommendation of Email Attachments

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          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.

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          Most cited references 29

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          A study of smoothing methods for language models applied to information retrieval

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            Principles of mixed-initiative user interfaces

             Eric Horvitz (1999)
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                Author and article information

                Journal
                16 October 2017
                Article
                10.1145/3132847.3132979
                1710.06061

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                CIKM2017. Proceedings of the 26th ACM International Conference on Information and Knowledge Management. 2017
                cs.IR cs.AI

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