Conversational Recommendations Using Model-Based Reasoning

Oliver A. Tazl, Alexander Perko, Franz Wotawa

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

Chatbots as conversational recommender have gained increasing importance over the years. The chatbot market offers a variety of applications for research and industry alike. In this paper, we discuss an implementation that supports the use of our recommendation algorithm during chatbot communication. The program eases communication and improves the underlying recommendation flow. In particular, the implementation makes use of our model-based reasoning approach for improving user experience during a chat, i.e., in cases where user configurations cause inconsistencies. The approach deals with such issues by removing inconsistencies in order to generate a valid recommendation. In addition to the underlying definitions, we demonstrate our implementation along use cases from the tourism domain.

Original languageEnglish
Title of host publicationProceedings of the 21th International Configuration Workshop
Subtitle of host publicationConfWS’19 - 21st Configuration Workshop
Pages13-19
Number of pages7
Volume2467
Publication statusPublished - 1 Jan 2019
Event21st International Configuration Workshop, ConfWS 2019 - Hamburg, Germany
Duration: 19 Sept 201920 Sept 2019

Publication series

NameCEUR Workshop Proceedings
PublisherRWTH Aachen
ISSN (Print)1613-0073

Conference

Conference21st International Configuration Workshop, ConfWS 2019
Country/TerritoryGermany
CityHamburg
Period19/09/1920/09/19

ASJC Scopus subject areas

  • General Computer Science

Fields of Expertise

  • Information, Communication & Computing

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