Efficient learning of email similarities for customer supportBakker, 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 proceeding › Conference contribution › Academic › peer-review
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.
|Title of host publication||27th European Symposium on Artificial Neural Networks, ESANN 2019|
|Number of pages||6|
|Publication status||Published - 24-Apr-2019|
|Event||The 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning|
: Bruges (Belgium), 24 - 26 April 2019 - Bruges , Belgium
Duration: 24-Apr-2019 → 26-Apr-2019
|Conference||The 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning|
|Period||24/04/2019 → 26/04/2019|
The 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
: Bruges (Belgium), 24 - 26 April 2019
24/04/2019 → 26/04/2019Bruges , Belgium