The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
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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“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]
Citations
Cites
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Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks
Quadrana, M., Karatzoglou, A., Hidasi, B., & Cremonesi, P. (2017) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3109859.3109896 · Cited by 757
Adaptive web search based on user profile constructed without any effort from users
Sugiyama, K., Hatano, K., & Yoshikawa, M. (2004) · International World Wide Web Conference (WWW) · doi:10.1145/988672.988764 · Cited by 645
Towards Conversational Recommender Systems
Christakopoulou, K., Radlinski, F., & Hofmann, K. (2016) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2939672.2939746 · Cited by 445
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de Souza, L. S. (2022) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.55.2022.tde-10102022-134457
User tests & techniques for the post-hoc explanation of deep learning
Delaney, E. D., Kenny, E. M., Greene, D., & Keane, M. T. (2023) · Elsevier eBooks · doi:10.1016/b978-0-32-396098-4.00019-3
MAWA: Enhancing mobile web browsing through web augmentation for improved user experience
Aldalur, I., Perez, A., Larrinaga, F., & Illarramendi, M. (2024) · SoftwareX · doi:10.1016/j.softx.2024.101633
Synthetic datasets for microwave dish mensuration via pose estimation in deep learning models
Liberatore, C., Marrs, C., Bielas, J., et al. (2024) · doi:10.1117/12.3013436
IVF sonification: making music to the rhythm of life
Savery, R. J., Gilboa, D., & Weinberg, G. (2025) · Interacting with Computers · doi:10.1093/iwc/iwaf023
Design and Architecture of AutoML for Data Science in Next‐Generation Industries
Gangadevi, E., Santhi, K., & Shri, M. L. (2025) · doi:10.1002/9781394272426.ch1
Sheng, Y., Gu, Y., Cao, J., Liu, Y., Wang, X., Chen, J., Sun, X., & Zhang, J. (2025) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2025.103592
Liu, Y., Cao, Q., Shen, H., Zhang, K., Wu, Y., & Cheng, X. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757414
Metadata
- 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
- 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
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