Abstract

A central concern in an interactive intelligent system is optimization of its actions, to be maximally helpful to its human user. In recommender systems for instance, the action is to choose what to recommend, and the optimization task is to recommend items the user prefers. The optimization is done based on earlier user's feedback (e.g. "likes" and "dislikes"), and the algorithms assume the feedback to be faithful. That is, when the user clicks “like,” they actually prefer the item. We argue that this fundamental assumption can be extensively violated by human users, who are not passive feedback sources. Instead, they are in control, actively steering the system towards their goal. To verify this hypothesis, that humans steer and are able to improve performance by steering, we designed a function optimization task where a human and an optimization algorithm collaborate to find the maximum of a 1-dimensional function. At each iteration, the optimization algorithm queries the user for the value of a hidden function f at a point x, and the user, who sees the hidden function, provides an answer about f(x). Our study on 21 participants shows that users who understand how the optimization works, strategically provide biased answers (answers not equal to f(x)), which results in the algorithm finding the optimum significantly faster. Our work highlights that next-generation intelligent systems will need user models capable of helping users who steer systems to pursue their goals.
Original languageEnglish
Title of host publicationProceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization
Subtitle of host publicationUMAP 2020
Place of PublicationNew York, NY, United States
PublisherACM
Pages293-297
Number of pages5
ISBN (Electronic)978-1-4503-6861-2
DOIs
Publication statusPublished - 13 May 2020
MoE publication typeA4 Article in a conference publication
EventConference on User Modeling, Adaptation and Personalization - Online, Genoa, Italy
Duration: 12 Jul 202018 Jul 2020
Conference number: 28

Conference

ConferenceConference on User Modeling, Adaptation and Personalization
Abbreviated titleUMAP
CountryItaly
CityGenoa
Period12/07/202018/07/2020

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    Mikko Hakala (Manager)

    School of Science

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  • Cite this

    Colella, F., Daee, P., Jokinen, J., Oulasvirta, A., & Kaski, S. (2020). Human Strategic Steering Improves Performance of Interactive Optimization. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization: UMAP 2020 (pp. 293-297). New York, NY, United States: ACM. https://doi.org/10.1145/3340631.3394883