Virtual bartender: a dialog system combining data-driven and knowledge-based recommendation

This research is about combination of data-driven and knowledge-based recommendations The research is made in an application scenario for whisky recommendation, where a guest chats with a recommender system. Preferences about taste are difficult to express and the knowledge about taste is tacit and thus can hardly be represented and used adequately. People or not aware of how to describe flavors in a standardized way and how to do a justified choice. This is because knowledge about taste is mainly tacit knowledge. To deal with this knowledge, data-driven recommendation is adequate. On the other hand, in particular experienced customers use knowledge about distilleries, locations and the distillery process to express their preferences and want to have arguments for the recommended products. This shows that a combination of data-driven and knowledge-based recommendations is appropriate in areas where tacit knowledge and explicit knowledge are available.

Schlagwörter

Fachgebiet (DDC)

330 - Wirtschaft

Projekt

Veranstaltung

AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)

Startdatum der Ausstellung

Enddatum der Ausstellung

Startdatum der Konferenz

25.03.2019

Enddatum der Konferenz

27.03.2019

Datum der letzten Prüfung

ISBN

ISSN

Sprache

Englisch

Während FHNW Zugehörigkeit erstellt

Ja

Zukunftsfelder FHNW

Publikationsstatus

Veröffentlicht

Begutachtung

Peer-Review der ganzen Publikation

Open Access-Status

Diamond

Lizenz

Zitation

Hinkelmann, K., Blaser, M., Faust, O., Horst, A., & Mehli, C. (2019). Virtual bartender: a dialog system combining data-driven and knowledge-based recommendation. In A. Martin, K. Hinkelmann, A. Gerber, D. Lenat, F. van Harmelen, & P. Clark (Eds.), Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019). https://doi.org/10.26041/fhnw-6603

Komplettanzeige