Activity Modeling in Email

Qadir, Ashequl; Gamon, Michael; Pantel, Patrick; Awadallah, Ahmed Hassan

doi:10.18653/v1/n16-1171

Cited by 12 publications

(3 citation statements)

References 17 publications

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“…Modeling Actions on Email. Our work is related to previous research on user behavior and email action modeling [10,11,20,23,24,28]. Dabbish et al [10] examined people's ratings of message importance and the actions they took on speci c email messages with a survey of 121 people at a university.…”

Section: Related Workmentioning

confidence: 93%

Characterizing and Predicting Enterprise Email Reply Behavior

Liu

Dumais

Bennett

et al. 2017

Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Email is still among the most popular online activities. People spend a signi cant amount of time sending, reading and responding to email in order to communicate with others, manage tasks and archive personal information. Most previous research on email is based on either relatively small data samples from user surveys and interviews, or on consumer email accounts such as those from Yahoo! Mail or Gmail. Much less has been published on how people interact with enterprise email even though it contains less automatically generated commercial email and involves more organizational behavior than is evident in personal accounts. In this paper, we extend previous work on predicting email reply behavior by looking at enterprise se ings and considering more than dyadic communications. We characterize the in uence of various factors such as email content and metadata, historical interaction features and temporal features on email reply behavior. We also develop models to predict whether a recipient will reply to an email and how long it will take to do so. Experiments with the publicly-available Avocado email collection show that our methods outperform all baselines with large gains. We also analyze the importance of different features on reply behavior predictions. Our ndings provide new insights about how people interact with enterprise email and have implications for the design of the next generation of email clients.

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Section: Related Workmentioning

confidence: 93%

Characterizing and Predicting Enterprise Email Reply Behavior

Liu

Dumais

Bennett

et al. 2017

Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

Self Cite

View full text Add to dashboard Cite

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“…Researchers have used machine learning methods to improve email efficiency by predicting email responses. Previous work includes predicting email importance and ranking by likelihood of user action (Aberdeen et al, 2010), classifying emails into common actions -read, reply, delete, and delete-WithoutRead (Di Castro et al, 2016), and characterizing response behavior based on various factors Kooti et al, 2015;Qadir et al, 2016) including time, length, and conversion, temporal, textual properties, and historical interactions. Our work differs from previous studies by considering both semantic and structural information in email response prediction and developing an interpretable model.…”

Section: Email Response Predictionmentioning

confidence: 99%

PROMINET: Prototype-based Multi-View Network for Interpretable Email Response Prediction

Wang,

Vijayaraghavan,

Degan

2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track

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Email is a widely used tool for business communication, and email marketing has emerged as a cost-effective strategy for enterprises. While previous studies have examined factors affecting email marketing performance, limited research has focused on understanding email response behavior by considering email content and metadata. This study proposes a Prototypebased Multi-view Network (PROMINET) that incorporates semantic and structural information from email data. By utilizing prototype learning, the PROMINET model generates latent exemplars, enabling interpretable email response prediction. The model maps learned semantic and structural exemplars to observed samples in the training data at different levels of granularity, such as document, sentence, or phrase. The approach is evaluated on two realworld email datasets: the Enron corpus and an in-house Email Marketing corpus. Experimental results demonstrate that the PROMINET model outperforms baseline models, achieving a ∼ 3% improvement in F 1 score on both datasets. Additionally, the model provides interpretability through prototypes at different granularity levels while maintaining comparable performance to non-interpretable models.The learned prototypes also show potential for generating suggestions to enhance email text editing and improve the likelihood of effective email responses. This research contributes to enhancing sender-receiver communication and customer engagement in email interactions.

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“…Carvalho and Cohen [12] classify emails according to their speech acts. Graus et al [20], Qadir et al [45] recommend recipients to send an email message to. Kannan et al [28] propose an end-to-end method for automatically generating email responses that can be sent by the user with a single click.…”

Section: Predictive Models For Emailmentioning

confidence: 99%

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Gysel

Mitra

Venanzi

et al. 2017

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

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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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Activity Modeling in Email

Cited by 12 publications

References 17 publications

Characterizing and Predicting Enterprise Email Reply Behavior

Characterizing and Predicting Enterprise Email Reply Behavior

PROMINET: Prototype-based Multi-View Network for Interpretable Email Response Prediction

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