Conversational recommendations using model-based reasoning

Oliver A. Tazl, Alexander Perko, Franz Wotawa

Research output: Contribution to journalConference articlepeer-review


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
Pages (from-to)13-19
Number of pages7
JournalCEUR Workshop Proceedings
Publication statusPublished - 1 Jan 2019
Event21st International Configuration Workshop, ConfWS 2019 - Hamburg, Germany
Duration: 19 Sept 201920 Sept 2019

ASJC Scopus subject areas

  • Computer Science(all)


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