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A Deep Learning Approach to Quantify Sequence Similarities of Historical Customer Data
Stefan Vamosi (Contributor)
Reutterer, T.
(Contributor)
Michael Platzer (Contributor)
Klaudius Kalcher (Contributor)
Institute for Marketing and Customer Analytics
Research Institute for Cryptoeconomics
Research Institute for Computational Methods
Activity
:
Talk or presentation
›
Science to science
Period
20 Jun 2019
→
22 Jun 2019
Event title
INFORMS Marketing Science Conference 2019
Event type
Unknown
Degree of Recognition
International
Austrian Classification of Fields of Science and Technology (ÖFOS)
502019 Marketing
502
502020 Market research
502052 Business administration
Related content
Projects
Al-Based Privacy-Preserving Big Data Sharing for Market Research (ANITA-ANonymous bIg daTA)
Project
:
Research funding