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At Your Service: Coffee Beans Recommendation From a Robot Assistant

Published: 10 November 2020 Publication History

Abstract

With advances in the field of machine learning, service robots are envisioned to become more present. The COVID-19 pandemic has accelerated this need. One such example would be coffee shops, which have become intrinsic to our everyday lives. Yet, serving an excellent cup of coffee is not trivial as a coffee blend typically comprises rich aromas, indulgent and unique flavours. Our work addresses this by proposing a computational model which recommends optimal coffee beans resulting from users' preferences. Given coffee properties (objective features), we apply different supervised learning techniques to predict coffee qualities (subjective features). We then consider an unsupervised learning method to analyse the relationship between coffee beans in the subjective feature space. Evaluated on a real coffee beans dataset based on digitised reviews, our results illustrate that the proposed computational model gives up to 92.7 percent recommendation accuracy for coffee prediction. From this, we propose how it can be deployed on a robot.

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With advances in machine learning, robot assistants will become more present in the hospitality industry. The COVID-19 pandemic has also highlighted the need to have more service robots in our everyday lives, to minimise the risk of human to-human transmission. One example would be coffee shops, which have become intrinsic to our everyday lives. Our work proposes a computational model that recommends optimal coffee beans resulting from the user's preferences. Given a set of coffee bean properties (objective features), we apply supervised learning techniques to predict coffee qualities (subjective features). We then consider an unsupervised learning method to analyse the relationship between coffee beans in the subjective feature space. Our results illustrate that the proposed computational model gives up to 92.7 percent recommendation accuracy for coffee beans prediction. We also propose how this model can be deployed on a service robot to reliably predict customers' coffee preferences.

References

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Junya Nakanishi, Itaru Kuramoto, Jun Baba, Ogawa Kohei, Yuichiro Yoshikawa, and Hiroshi Ishiguro. 2018. Can a Humanoid Robot Engage in Heartwarming Interaction Service at a Hotel?. In Proceedings of the 6th International Conference on Human-Agent Interaction (Southampton, United Kingdom) (HAI '18). Association for Computing Machinery, New York, NY, USA, 45--53. https://doi.org/10.1145/3284432.3284448
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Junya Nakanishi, Itaru Kuramoto, Jun Baba, Kohei Ogawa, Yuichiro Yoshikawa, and Hiroshi Ishiguro. 2020. Continuous Hospitality with Social Robots at a hotel. SN Applied Sciences, Vol. 2 (03 2020). https://doi.org/10.1007/s42452-020--2192--7
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Andrew Parsons and Ann-Marie Kennedy. 2009. Wine recommendations: Who do I believe? British Food Journal, Vol. 111 (09 2009), 1003--1015. https://doi.org/10.1108/00070700910992899
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Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, Vol. 15, 1 (2014), 1929--1958.
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Cited By

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  • (2023)A Study of the Physical Characteristics and Defects of Green Coffee Beans That Influence the Sensory Notes Using Machine Learning ModelsProcesses10.3390/pr1201001812:1(18)Online publication date: 20-Dec-2023
  • (2023)Feeding the Coffee Habit: A Longitudinal Study of a Robo-Barista2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)10.1109/RO-MAN57019.2023.10309621(1983-1990)Online publication date: 28-Aug-2023
  • (2023)Classification of Arabica Coffee Beans Based on Multi-Features Using Artificial Neural Networks2023 1st International Conference on Advanced Engineering and Technologies (ICONNIC)10.1109/ICONNIC59854.2023.10467549(85-90)Online publication date: 14-Oct-2023

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  1. At Your Service: Coffee Beans Recommendation From a Robot Assistant

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    cover image ACM Conferences
    HAI '20: Proceedings of the 8th International Conference on Human-Agent Interaction
    November 2020
    304 pages
    ISBN:9781450380546
    DOI:10.1145/3406499
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Published: 10 November 2020

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    Author Tags

    1. hospitality
    2. personalisation
    3. recommendation system
    4. service robots

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    View all
    • (2023)A Study of the Physical Characteristics and Defects of Green Coffee Beans That Influence the Sensory Notes Using Machine Learning ModelsProcesses10.3390/pr1201001812:1(18)Online publication date: 20-Dec-2023
    • (2023)Feeding the Coffee Habit: A Longitudinal Study of a Robo-Barista2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)10.1109/RO-MAN57019.2023.10309621(1983-1990)Online publication date: 28-Aug-2023
    • (2023)Classification of Arabica Coffee Beans Based on Multi-Features Using Artificial Neural Networks2023 1st International Conference on Advanced Engineering and Technologies (ICONNIC)10.1109/ICONNIC59854.2023.10467549(85-90)Online publication date: 14-Oct-2023

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