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.

Keywords

Subject (DDC)

330 - Wirtschaft

Project

Event

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

Exhibition start date

Exhibition end date

Conference start date

25.03.2019

Conference end date

27.03.2019

Date of the last check

ISBN

ISSN

Language

English

Created during FHNW affiliation

Yes

Strategic action fields FHNW

Publication status

Published

Review

Peer review of the complete publication

Open access category

Diamond

License

Citation

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

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