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An investigation on the user interaction modes of conversational recommender systems for the music domain

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Abstract

Conversational Recommender Systems (CoRSs) implement a paradigm that allows users to interact in natural language with the system for defining their preferences and discovering items that best fit their needs. CoRSs can be straightforwardly implemented as chatbots that, nowadays, are becoming more and more popular for several applications, such as customer care, health care, and medical diagnoses. Chatbots implement an interaction based on natural language, buttons, or both. The implementation of a chatbot is a challenging task since it requires knowledge about natural language processing and human–computer interaction. A CoRS might be particularly useful in the music domain since music is generally enjoyed in contexts when a standard interface cannot be exploited (driving, doing homeworks, running). However, there is no work in the literature that analytically compares different interaction modes for a conversational music recommender system. In this paper, we focus on the design and implementation of a CoRS for the music domain. Our CoRS consists of different components. The system implements content-based recommendation, critiquing and adaptive strategies, as well as explanation facilities. The main innovative contribution is that the user can interact through different interaction modes: natural language, buttons, and mixed. Due to the lack of available datasets for testing CoRSs, we carried out an in vivo experimental evaluation with the goal of investigating the impact of the different interaction modes on the recommendation accuracy and on the cost of interaction for the final user. The experiment involved 110 people, and 54 completed the whole process. The analysis of the results shows that the best interaction mode is based on a mixed strategy that combines buttons and natural language. In addition, the results allow to clearly understand which are the steps in the dialog that are particularly strenuous for the user.

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Notes

  1. Visual Capitalist, What Happens in an Internet Minute in 2017?, http://www.visualcapitalist.com/happens-internet-minute-2017/.

  2. IFPI, Connecting with music consumer—Insight report, http://ifpi.org/downloads/Music-Consumer-Insight-Report-2017.pdf.

  3. https://techcrunch.com/2019/01/15/pandora-launches-a-personalized-voice-assistant-on-ios-and-android/.

  4. Wikipedia, Wikivoyage, Wikisource, and others.

  5. https://stanfordnlp.github.io/CoreNLP/.

  6. http://wiki.dbpedia.org/.

  7. Please not be confused with MAP.

  8. http://hci.epfl.ch/research-projects/resque/.

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Acknowledgements

This work is partially funded by Project Electronic Shopping & Home delivery of Edible goods with Low environmental Footprint (ESHELF), under the Apulian INNONETWORK programme - Italy, and by OBJECTWAY SPA under the project Unified Wealt Management Platform. The authors also want to thank Andrea Iovine for the support given to this research.

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  1. Department of Computer Science, University of Bari Aldo Moro, Bari, Italy

    Fedelucio Narducci, Pierpaolo Basile, Marco de Gemmis, Pasquale Lops & Giovanni Semeraro

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  1. Fedelucio Narducci
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Correspondence to Fedelucio Narducci.

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Appendices

Questionnaire

See Table 4.

Table 4 Questionnaire
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Anova test results

See Tables 5, 6, and 7.

Table 5 Degrees of freedoms, F-score, p value, partial \({\eta }^2\) for each metric
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Table 6 Average value for each metric and interaction mode
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Table 7 Standard deviation for each metric and interaction mode
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Narducci, F., Basile, P., de Gemmis, M. et al. An investigation on the user interaction modes of conversational recommender systems for the music domain. User Model User-Adap Inter 30, 251–284 (2020). https://doi.org/10.1007/s11257-019-09250-7

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