  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 Prototype-based 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 real-world 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 F1 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.

Computation and Language;Artificial Intelligence;Machine Learning