A Content-Based Approach to Email Triage Action Prediction: Exploration and Evaluation

by   Sudipto Mukherjee, et al.

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.


page 1

page 2

page 3

page 4


Item Recommendation Using User Feedback Data and Item Profile

Matrix factorization (MS) is a collaborative filtering (CF) based approa...

Random Walks with Erasure: Diversifying Personalized Recommendations on Social and Information Networks

Most existing personalization systems promote items that match a user's ...

Content-Based Personalized Recommender System Using Entity Embeddings

Recommender systems are a class of machine learning algorithms that prov...

Effects of Foraging in Personalized Content-based Image Recommendation

A major challenge of recommender systems is to help users locating inter...

Deep Joint Embeddings of Context and Content for Recommendation

This paper proposes a deep learning-based method for learning joint cont...

The Elements of Visual Art Recommendation: Learning Latent Semantic Representations of Paintings

Artwork recommendation is challenging because it requires understanding ...

Optimal Action-based or User Prediction-based Haptic Guidance: Can You Do Even Better?

The recently advanced robotics technology enables robots to assist users...

Please sign up or login with your details

Forgot password? Click here to reset