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Improving the User Experience with a Conversational Recommender System

Part of the Lecture Notes in Computer Science book series (LNAI,volume 11298)

Abstract

Chatbots are becoming more and more popular for several applications like customer care, health care, medical diagnoses. Generally, they have an interaction with users based on natural language, buttons, or both. In this paper we study the user interaction with a content-based recommender system implemented as a Telegram chatbot. More specifically, we investigate on one hand what are the best strategies for reducing the cost of interaction for the users and, on the other hand how to improve their experience. Our chatbot is able to provide personalized recommendations in the movie domain and implements critiquing strategies for improving the recommendation accuracy as well. In a preliminary experimental evaluation, carried out through a user study, interesting results emerged.

Keywords

  • Conversational recommender system
  • Chatbot
  • User experience

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  • DOI: 10.1007/978-3-030-03840-3_39
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Fig. 1.
Fig. 2.
Fig. 3.

Notes

  1. 1.

    The chatbot can be tested by searching for @MovieRecSysBot in Telegram list of contacts.

  2. 2.

    http://wiki.dbpedia.org/.

  3. 3.

    The diversity is computed by the Jaccard index on the movie properties between the items in the user profile and the items not rated yet.

  4. 4.

    @MovieRecBot.

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Acknowledgment

This work has been funded by the projects UNIFIED WEALTH MANAGEMENT PLATFORM - OBJECTWAY SpA - Via Giovanni Da Procida nr. 24, 20149 MILANO - c.f., P. IVA 07114250967, and PON01 00850 ASK-Health (Advanced system for the interpretations and sharing of knowledge in health care).

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Correspondence to Fedelucio Narducci .

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Narducci, F., de Gemmis, M., Lops, P., Semeraro, G. (2018). Improving the User Experience with a Conversational Recommender System. In: Ghidini, C., Magnini, B., Passerini, A., Traverso, P. (eds) AI*IA 2018 – Advances in Artificial Intelligence. AI*IA 2018. Lecture Notes in Computer Science(), vol 11298. Springer, Cham. https://doi.org/10.1007/978-3-030-03840-3_39

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  • DOI: https://doi.org/10.1007/978-3-030-03840-3_39

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