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UQ-AAS21: A Comprehensive Dataset of Amazon Alexa Skills

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Advanced Data Mining and Applications (ADMA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13087))

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Abstract

Various virtual personal assistant (VPA) services have become popular, due to the convenient interaction manner of voice user interface (VUI) they offer. Centered around them, an ecosystem involving service providers, third-party developers and end users, has started being formulated. The developers are enabled to create applications and release them through application stores, from which the users can obtain them and then run them on smart devices. This emerging ecosystem is still in its early stage, and a great deal of research effort is desired to make it on the healthy track to facilitate its development. Nonetheless, there is still a lack of comprehensive datasets for our research community to conduct research on relevant issues, e.g., the bug-freeness and quality of the applications, and users’ security and privacy concerns on them. In this work, we aim to build such a dataset for research use. We target the Amazon VPA service, i.e., the Alexa, which is the most popular VPA service. We collect 65,195 Alexa applications (or skills), and extract comprehensive information about them, including invocation names, user reviews, among overall 16 attributes. We show the demographic details of the skills and their developers, and also conduct preliminary statistical analyses on the quality and privacy issues, to demonstrate the potential usage of our dataset. The dataset and analysis results are released online to facilitate future research: https://github.com/xie00059/Amazon-Alexa-UQ-AAS21-datasets.

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Notes

  1. 1.

    The popularity of a skill is measured based on the number of ratings it receives.

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Authors and Affiliations

  1. The University of Queensland, Brisbane, Australia

    Fuman Xie, Yanjun Zhang, Hanlin Wei & Guangdong Bai

Authors
  1. Fuman Xie
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  2. Yanjun Zhang
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  3. Hanlin Wei
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  4. Guangdong Bai
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Corresponding author

Correspondence to Yanjun Zhang .

Editor information

Editors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Bohan Li

  2. University of Queensland, Brisbane, QLD, Australia

    Lin Yue

  3. University of Technology Sydney, Sydney, NSW, Australia

    Jing Jiang

  4. University of Queensland, Brisbane, QLD, Australia

    Weitong Chen

  5. University of Queensland, Brisbane, QLD, Australia

    Xue Li

  6. University of Technology Sydney, Sydney, NSW, Australia

    Guodong Long

  7. Carnegie Mellon University, Pittsburgh, USA

    Fei Fang

  8. Nanyang Technological University, Singapore, Singapore

    Han Yu

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Xie, F., Zhang, Y., Wei, H., Bai, G. (2022). UQ-AAS21: A Comprehensive Dataset of Amazon Alexa Skills. In: Li, B., et al. Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13087. Springer, Cham. https://doi.org/10.1007/978-3-030-95405-5_12

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  • DOI: https://doi.org/10.1007/978-3-030-95405-5_12

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  • Print ISBN: 978-3-030-95404-8

  • Online ISBN: 978-3-030-95405-5

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