Abstract
With the growing popularity of intelligent assistants (IAs), evaluating IA quality becomes an increasingly active field of research. This paper identifies and quantifies the feedback effect, a novel component in IA-user interactions – how the capabilities and limitations of the IA influence user behavior over time. First, we demonstrate that unhelpful responses from the IA cause users to delay or reduce subsequent interactions in the short term via an observational study. Next, we expand the time horizon to examine behavior changes and show that as users discover the limitations of the IA’s understanding and functional capabilities, they learn to adjust the scope and wording of their requests to increase the likelihood of receiving a helpful response from the IA. Our findings highlight the impact of the feedback effect at both the micro and meso levels. We further discuss its macro-level consequences: unsatisfactory interactions continuously reduce the likelihood and diversity of future user engagements in a feedback loop.
Y. Zhang—Contributions made during the internship at Apple in the summer of 2022.
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Notes
- 1.
This value is not necessarily a reflection of the aggregated or expected satisfaction metric, due to the sampling method and potential bias in the subpopulation of choice.
- 2.
The IA helpfulness of a given user request is defined as the user’s satisfaction with the IA’s response to the request, as determined by human annotators. .
- 3.
Supplemental Materials: https://machinelearning.apple.com/research/feedback-effect.
- 4.
Propensity weighting methods: https://cran.r-project.org/web/packages/PSweight.
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Acknowledgements
This work was made possible by Zak Aldeneh, Russ Webb, Barry Theobald, Patrick Miller, Julia Lin, Tony Y. Li, Leneve Gorbaty, Jessica Maria Echterhof and many others at Apple. We also thank Ricardo Henao and Shuxi Zeng at Duke University for their support and feedback.
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Xiu, Z. et al. (2023). Feedback Effect in User Interaction with Intelligent Assistants: Delayed Engagement, Adaption and Drop-out. In: Kashima, H., Ide, T., Peng, WC. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2023. Lecture Notes in Computer Science(), vol 13936. Springer, Cham. https://doi.org/10.1007/978-3-031-33377-4_12
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