Vamosi, Stefan, Reutterer, Thomas, Platzer, Michael, Kalcher, Klaudius. 2019. A Deep Learning Approach to Quantify Sequence Similarities of Historical Customer Data. INFORMS Marketing Science Conference 2019, Rom, Italien, 20.06.-22.06.
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| Status of publication | Published |
|---|---|
| Affiliation | External |
| Type of publication | Paper presented at an academic conference or symposium |
| Language | English |
| Title | A Deep Learning Approach to Quantify Sequence Similarities of Historical Customer Data |
| Event | INFORMS Marketing Science Conference 2019 |
| Year | 2019 |
| Date | 20.06.-22.06. |
| Country | Italy |
| Location | Rom |
Associations
- Projects
- Al-Based Privacy-Preserving Big Data Sharing for Market Research (ANITA-ANonymous bIg daTA)
- People
- Vamosi, Stefan (Details)
- Reutterer, Thomas (Details)
- External
- Kalcher, Klaudius (Mostly Al, Founder & Chief Data Scientist, Austria)
- Platzer, Michael (Mostly Al, Austria)
- Organization
- Institute for Marketing and Customer Analytics IN (Details)
- Research Institute for Computational Methods FI (Details)
- Research Institute for Cryptoeconomics FI (Details)
- Research areas (ÖSTAT Classification 'Statistik Austria')
- 5301 Distributive trades (Details)
- 5307 Business and management economics (Details)
- 5315 Commercial science (Details)
- 5320 Marketing (Details)
- 5321 Market research (Details)