Learning User Embeddings from Emails
Many important email-related tasks, such as email classification or search, highly rely on building quality document representations (e.g., bag-of-words or key phrases) to assist matching and understanding. Despite prior success on representing textual messages, creating quality user representations from emails was overlooked. In this paper, we propose to represent users using embeddings that are trained to reflect the email communication network. Our experiments on Enron dataset suggest that the resulting embeddings capture the semantic distance between users. To assess the quality of embeddings in a real-world application, we carry out auto-foldering task where the lexical representation of an email is enriched with user embedding features. Our results show that folder prediction accuracy is improved when embedding features are present across multiple settings.
Code (0)
등록된 구현이 없습니다.
Tasks
General ClassificationRecommendation SystemsWord EmbeddingsSimilar Papers 제목 키워드 기반
Phishing Detection through Email Embeddings
The problem of detecting phishing emails through machine learning techniques has been discussed extensively in the literature. Conventional and state-of-the-art machine learning algorithms have demonstrated the possibili…
BIG-bench Machine LearningUsing item recommendations and LLMs in marketing email titles
E-commerce marketplaces make use of a number of marketing channels like emails, push notifications, etc. to reach their users and stimulate purchases. Personalized emails especially are a popular touch point for marketer…
Characterizing and Predicting Email Deferral Behavior
Email triage involves going through unhandled emails and deciding what to do with them. This familiar process can become increasingly challenging as the number of unhandled email grows. During a triage session, users com…
Topic Modeling Using Distributed Word Embeddings
We propose a new algorithm for topic modeling, Vec2Topic, that identifies the main topics in a corpus using semantic information captured via high-dimensional distributed word embeddings. Our technique is unsupervised an…
Word EmbeddingsEmail Classification Incorporating Social Networks and Thread Structure
Existing methods for different document classification tasks in the context of social networks typically only capture the semantics of texts, while ignoring the users who exchange the text and the network they form. Howe…
ClassificationDocument ClassificationGeneral ClassificationSentiment Analysis