Publication

Efficient learning of email similarities for customer support

Bakker, J. & Bunte, K., 24-Apr-2019, 27th European Symposium on Artificial Neural Networks, ESANN 2019. Verleysen, M. (ed.). d-side publishing, p. 119-124 6 p.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

One way to increase customer satisfaction is efficient and consistent customer email support. In this contribution we investigate the use
of dimensionality reduction, metric learning and classification methods to
predict answer templates that can be used by an employee or retrieve historic conversations with potential suitable answers given an email query.
The strategies are tested on email data and the publicly available Reuters
data. We conclude that prototype-based metric learning is fast to train
and the parameters provide a compressed representation of the database
enabling efficient content based retrieval. Furthermore, learning customer
email embedings based on the similarity of employee answers is a promising direction for computer aided customer support.
Original languageEnglish
Title of host publication27th European Symposium on Artificial Neural Networks, ESANN 2019
EditorsMichel Verleysen
Publisherd-side publishing
Pages119-124
Number of pages6
ISBN (Print)978-287-587-065-0
Publication statusPublished - 24-Apr-2019
EventThe 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
: Bruges (Belgium), 24 - 26 April 2019
- Bruges , Belgium
Duration: 24-Apr-201926-Apr-2019

Conference

ConferenceThe 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
CountryBelgium
CityBruges
Period24/04/201926/04/2019

Event

The 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
: Bruges (Belgium), 24 - 26 April 2019

24/04/201926/04/2019

Bruges , Belgium

Event: Conference

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