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Interactive Intent Modeling from Multiple Feedback Domains

Published: 07 March 2016 Publication History

Abstract

In exploratory search, the user starts with an uncertain information need and provides relevance feedback to the system's suggestions to direct the search. The search system learns the user intent based on this feedback and employs it to recommend novel results. However, the amount of user feedback is very limited compared to the size of the information space to be explored. To tackle this problem, we take into account user feedback on both the retrieved items (documents) and their features (keywords). In order to combine feedback from multiple domains, we introduce a coupled multi-armed bandits algorithm, which employs a probabilistic model of the relationship between the domains. Simulation results show that with multi-domain feedback, the search system can find the relevant items in fewer iterations than with only one domain. A preliminary user study indicates improvement in user satisfaction and quality of retrieved information.

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  • (2023)User Feedback-based Online Learning for Intent ClassificationProceedings of the 25th International Conference on Multimodal Interaction10.1145/3577190.3614137(613-621)Online publication date: 9-Oct-2023
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      cover image ACM Conferences
      IUI '16: Proceedings of the 21st International Conference on Intelligent User Interfaces
      March 2016
      446 pages
      ISBN:9781450341370
      DOI:10.1145/2856767
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 07 March 2016

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      Author Tags

      1. exploratory search
      2. intent modeling
      3. multi-armed bandits
      4. probabilistic user models
      5. relevance feedback

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      • Re:Know funded by TEKES
      • European Union in the Seventh Framework Programme

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      IUI '16 Paper Acceptance Rate 49 of 194 submissions, 25%;
      Overall Acceptance Rate 746 of 2,811 submissions, 27%

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      Cited By

      View all
      • (2024)On the Negative Perception of Cross-domain Recommendations and ExplanationsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657735(2102-2113)Online publication date: 10-Jul-2024
      • (2023)User Feedback-based Online Learning for Intent ClassificationProceedings of the 25th International Conference on Multimodal Interaction10.1145/3577190.3614137(613-621)Online publication date: 9-Oct-2023
      • (2022)EntityBot: Actionable Entity Recommendations for Everyday Digital TaskExtended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491101.3519910(1-4)Online publication date: 27-Apr-2022
      • (2021)EntityBot: Supporting Everyday Digital Tasks with Entity RecommendationsProceedings of the 15th ACM Conference on Recommender Systems10.1145/3460231.3478883(753-756)Online publication date: 13-Sep-2021
      • (2021)Exploratory Search of GANs with Contextual BanditsProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482103(3157-3161)Online publication date: 26-Oct-2021
      • (2021)Entity Recommendation for Everyday Digital TasksACM Transactions on Computer-Human Interaction10.1145/345891928:5(1-41)Online publication date: 20-Aug-2021
      • (2020)Introduction to Bandits in Recommender SystemsProceedings of the 14th ACM Conference on Recommender Systems10.1145/3383313.3411547(748-750)Online publication date: 22-Sep-2020
      • (2020)Human Strategic Steering Improves Performance of Interactive OptimizationProceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3340631.3394883(293-297)Online publication date: 7-Jul-2020
      • (2019)Bandit algorithms in recommender systemsProceedings of the 13th ACM Conference on Recommender Systems10.1145/3298689.3346956(574-575)Online publication date: 10-Sep-2019
      • (2019)May AI?Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems10.1145/3290605.3300863(1-12)Online publication date: 2-May-2019
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