- Titel
- Modeling Customers and Products with Word Embeddings from Receipt Data
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-806335
- Konferenz
- IDEAS 2018: 22nd International Database Engineering & Applications Symposium. Villa San Giovanni, 18. - 20. Juni 2018
- Quellenangabe
- IDEAS 2018: Proceedings of the 22nd International Database Engineering & Applications SymposiumHerausgeber: Bipin C. Desai
Herausgeber: Sergio Flesca
Herausgeber: Ester Zumpano
Herausgeber: Elio Masciari
Herausgeber: Luciano Caroprese
Erscheinungsort: New York
Verlag: ACM
Erscheinungsjahr: 2018
Seiten: 246-252
ISBN: 978-1-4503-6527-7 - Erstveröffentlichung
- 2018
- Abstract (EN)
- For many tasks in market research it is important to model customers and products as comparable instances. Usually, the integration of customers and products into one model is not trivial. In this paper, we will detail an approach for a combined vector space of customers and products based on word embeddings learned from receipt data. To highlight the strengths of this approach we propose four different applications: recommender systems, customer and product segmentation and purchase prediction. Experimental results on a real-world dataset with 200M order receipts for 2M customers show that our word embedding approach is promising and helps to improve the quality in these applications scenarios.
- Andere Ausgabe
- Link zum Artikel, der zuerst in der ACM Digital Library erschienen ist.
DOI: 10.1145/3216122.3229860 - Freie Schlagwörter (DE)
- Worteinbindung, Empfehlungssysteme, Segmentierung, Anwendungen, word2vec
- Freie Schlagwörter (EN)
- word embedding, recommender systems, segmentation, applications, word2vec
- Klassifikation (DDC)
- 004
- Verlag
- ACM, New York
- Version / Begutachtungsstatus
- angenommene Version / Postprint / Autorenversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-806335
- Veröffentlichungsdatum Qucosa
- 15.09.2022
- Dokumenttyp
- Konferenzbeitrag
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
