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Identifying factors that influence the acceptability of smart devices: implications for recommendations

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Abstract

This paper presents results from a web-based study that investigates users’ attitudes toward smart devices, focusing on acceptability. Specifically, we conducted a survey that elicits users’ ratings of devices in isolation and devices in the context of tasks potentially performed by these devices. Our study led to insights about users’ attitudes towards devices in isolation and in the context of tasks, and about the influence of demographic factors and factors pertaining to technical expertise and experience with devices on users’ attitudes. The insights about users’ attitudes provided the basis for two recommendation approaches based on principal components analysis (PCA) that alleviate the new-user and new-item problems: (1) employing latent features identified by PCA to predict ratings given by existing users to new devices, and by new users to existing devices; and (2) identifying a relatively small set of key questions on the basis of PCs, whose answers account to a large extent for new users’ ratings of devices in isolation and in the context of tasks. Our results show that taking into account latent features of devices, and asking a relatively small number of key questions about devices in the context of tasks, lead to rating predictions that are significantly more accurate than global and demographic predictions, and substantially reduce prediction error, eventually matching the performance of strong baselines.

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Notes

  1. We distinguish between acceptability, which takes place prior to usage, and acceptance, which is related to trust, and happens after a user has been exposed to a device (Verberne et al. 2012). However, acceptability is influenced by the available information, and may change over time.

  2. As noted in Sect. 5, owing to budgetary limitations, we could only perform a preliminary assessment of the application of PCA-LR to new users.

  3. A survey of active learning approaches to address cold-start problems appears in (Rubens et al. 2015).

  4. Our study did not feature speech-enabled assistants, e.g., Amazon’s Echo or OK Google, as such devices were not commonly available at the time our study began. Further, we assumed that in principle, any device can be endowed with a spoken communication capability, so this was not a distinguishing feature. However, our study included questions regarding preferred communication modalities, whose results are reported in (Zhan and Zukerman 2016).

  5. In retrospect, we should have asked about ethnicity, rather than country of residence.

  6. There is a 0.998 correlation between the device-only ratings of the 136 participants who completed only the first two segments of the survey and the device-only ratings of the 94 participants who completed the entire survey. However, owing to this difference in populations, the device-only results in this paper differ slightly from those in our UMAP’2016 paper (Zhan et al. 2016), which were obtained from 93 participants.

  7. Autonomous tasks were removed from this comparison owing to their low ratings.

  8. These numbers are slightly different from those in Fig. 4a, which were computed for 136 participants.

  9. An interesting exercise involves studying the impact of having a user rate fewer than 13 devices when calculating the PC weights for the remaining device. Notice, however, that the results of this exercise would depend on the way in which the devices are selected.

  10. The removal of these tasks yields different PCs, and hence different key questions, from those in our UMAP’2016 paper (Zhan et al. 2016). Nonetheless, the performance of the KeyQ-based methods (Sect. 7) is consistent with that obtained in the UMAP’2016 paper.

  11. Other PC-inspection protocols are possible, e.g., preferring higher-impact PCs.

  12. We employed SVD with Ky Fan norms, sourced from www.mathworks.com/matlabcentral/fileexchange/48406-svd-free-matrix-completion-for-recommender-system-design.

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Acknowledgements

This material is based upon work supported by the Air Force Office of Scientific Research, Asian Office of Aerospace Research and Development (AOARD), under award number FA2386-14-1-0010. The authors thank Gwyneth Rees and Masud Moshtaghi for their help in the initial stages of this research, and the three anonymous reviewers for their helpful comments.

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  1. Faculty of Information Technology, Monash University, Clayton, 3800, Australia

    Kai Zhan, Ingrid Zukerman & Andisheh Partovi

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  1. Kai Zhan
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Correspondence to Ingrid Zukerman.

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This paper significantly expands, adds detail to and revises some of the analysis in the paper entitled “Eliciting Users’ Attitudes toward Smart Devices”, co-authored by Kai Zhan, Ingrid Zukerman, Masud Moshtaghi and Gwyneth Rees, published in UMAP’2016 Proceedings: the User Modeling, Adaptation and Personalization Conference, pp. 175–184, Halifax, Nova Scotia, Canada, https://doi.org/10.1145/2930238.2930241. This paper or a similar version is not currently under review by a journal or conference. This paper is void of plagiarism or self-plagiarism as defined by the Committee on Publication Ethics and Springer Guidelines.

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Zhan, K., Zukerman, I. & Partovi, A. Identifying factors that influence the acceptability of smart devices: implications for recommendations. User Model User-Adap Inter 28, 391–423 (2018). https://doi.org/10.1007/s11257-018-9210-0

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