Proceedings of the 25th International Conference on Intelligent User Interfaces
Abstract
We present Navigation-by-Preference, n-by-p, a new conversational recommender that uses what the literature calls preference-based feedback.Given a seed item, the recommender helps the user navigate through item space to find an item that aligns with her longterm preferences (revealed by her user profile) but also satisfies her ephemeral, short-term preferences (revealed by the feedback she gives during the dialog).Different from previous work on preference-based feedback, n-by-p does not assume structured item descriptions (such as sets of attribute-value pairs) but works instead in the case of unstructured item descriptions (such as sets of keywords or tags), thus extending preference-based feedback to new domains where structured item descriptions are not available.Different too is that it can be configured to ignore long-term preferences or to take them into account, to work only on positive feedback or to also use negative feedback, and to take previous rounds of feedback into account or to use just the most recent feedback.We use an offline experiment with simulated users to compare 60 configurations of n-by-p.We find that a configuration that includes long-term preferences, that uses both positive and negative feedback, and that uses previous rounds of feedback is the one with highest hit-rate.It also obtains the best survey responses and lowest measures of effort in a trial with real users that we conducted with a web-based system.Notable too is that the user trial has a novel protocol for experimenting with short-term preferences.
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Individuals tend to rely more heavily on AI systems when confronted with more complex tasks.
“Additionally, prior work has demonstrated that individuals tend to rely more heavily on AI systems when confronted with more complex tasks [28] due to the challenges associated with analyzing large volumes of information [23].”
From Dealing with Uncertainty: Understanding the Impact of Prognostic Versus Diagnostic Tasks on Trust and Reliance in Human-AI Decision Making (2024) · cites this paper as [28]
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Li, F. M., Liu, M. X., Zhang, Y., & Carrington, P. (2022) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3517428.3544814 · Cited by 31 · see citing works
Exploring university students’ acceptability of autonomous vehicles and urban air mobility
Yavuz, Y. (2024) · Journal of Air Transport Management · doi:10.1016/j.jairtraman.2024.102546 · Cited by 24 · see citing works
Zhou, X., Liang, P., Zhang, B., Li, Z., Ahmad, A., Shahin, M., & Waseem, M. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112204 · Cited by 30 · see citing works
Zhu, G., Chen, Y., & Zheng, J. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102698 · Cited by 26 · see citing works
Achieving flatness: Graph labeling can generate graphical honeywords
Tian, Y., Li, L., Peng, H., et al. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102212 · Cited by 26 · see citing works
Preference prediction based on a photo gallery analysis with scene recognition and object detection
Savchenko, A., Demochkin, K. V., & Grechikhin, I. S. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108248 · Cited by 27 · see citing works
Zhang, H., Chen, P., Xie, X., Jiang, Z., Zhou, Z., & Sun, L. (2024) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3689433 · Cited by 30 · see citing works
Corbett, E., & Denton, E. (2023) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3593013.3594104 · Cited by 25 · see citing works
A multivocal literature review on the benefits and limitations of industry-leading AutoML tools
Quaranta, L., Azevedo, K., Calefato, F., & Kalinowski, M. (2024) · Information and Software Technology · doi:10.1016/j.infsof.2024.107608 · Cited by 24 · see citing works
Counterfactual explanations for misclassified images: How human and machine explanations differ
Delaney, E. D., Pakrashi, A., Greene, D., & Keane, M. T. (2023) · Artificial Intelligence · doi:10.1016/j.artint.2023.103995 · Cited by 24 · see citing works
Hasan, M., Vasker, N., & Khan, M. (2024) · Journal of Agriculture and Food Research · doi:10.1016/j.jafr.2024.101372 · Cited by 22 · see citing works
Designing user-centric programming aids for kinesthetic teaching of collaborative robots
Ajaykumar, G., Stiber, M., & Huang, C. (2021) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2021.103845 · Cited by 22 · see citing works
Jin, Y., Ma, M., & Liu, Y. (2023) · Journal on Computing and Cultural Heritage · doi:10.1145/3627164 · Cited by 22 · see citing works
Larasati, R. A., Liddo, A. D., & Motta, E. (2023) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3631614 · Cited by 21 · see citing works
Misuse or abuse of automation? Exploring drivers’ intentions to nap during automated driving
Wörle, J., & Metz, B. (2023) · Transportation Research Part F Traffic Psychology and Behaviour · doi:10.1016/j.trf.2023.10.023 · Cited by 21 · see citing works
AdaptiveVoice: Cognitively Adaptive Voice Interface for Driving Assistance
Wen, S., Ping, S., Wang, J., Liang, H., Xu, X., & Yan, Y. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642876 · Cited by 20 · see citing works
Kim, S., Choo, S., Park, D., Park, H., Nam, C. S., Jung, J., & Lee, S. (2023) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2023.103009 · Cited by 21 · see citing works
Chen, Q. Q., Lin, L. M., & Yi, Y. (2025) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2025.104281 · Cited by 21 · see citing works
A scalable decision-tree-based method to explain interactions in dyadic data
Eiras‐Franco, C., Guijarro‐Berdiñas, B., Alonso‐Betanzos, A., et al. (2019) · Decision Support Systems · doi:10.1016/j.dss.2019.113141 · Cited by 19 · see citing works
How and why does official information become misinformation? A typology of official misinformation
Ruokolainen, H., Widén, G., & Eskola, E.-L. (2023) · Library & Information Science Research · doi:10.1016/j.lisr.2023.101237 · Cited by 18 · see citing works
Collage is the New Writing: Exploring the Fragmentation of Text and User Interfaces in AI Tools
Buschek, D. (2024) · Designing Interactive Systems Conference · doi:10.1145/3643834.3660681 · Cited by 20 · see citing works
Wardatzky, K., Inel, O., Rossetto, L., & Bernstein, A. (2025) · ACM Transactions on Recommender Systems · doi:10.1145/3716394 · Cited by 20 · see citing works
Towards accessible website design through artificial intelligence: A systematic literature review
Vera-Amaro, G., & Rojano-Cáceres, J. R. (2025) · Information and Software Technology · doi:10.1016/j.infsof.2025.107821 · Cited by 21 · see citing works
- Publisher
- ACM
- Published
- 2020-03-17
- OpenAlex
- W190293641
- Cited by
- 209
- References
- 30
- Citations by year
- 2025: 48 · 2024: 41 · 2023: 41 · 2022: 41 · 2021: 25 · 2020: 11 · 2019: 2
- Type
- paratext
- Language
- en
- Open access
- closed
- Funders
- Science Foundation Ireland (SFI/12/RC/, SFI/12/RC/2289., 12/RC/, Grant Number SFI/12/RC/2289, 12/RC/2289)
- European Regional Development Fund (SFI/12/RC/2289)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Keywords
- conversational recommender systems, user profiles, negative feedback, user effort, user interface design
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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