short-paper

Interactive Intent Modeling from Multiple Feedback Domains

Authors:

Dorota Glowacka,

Samuel KaskiAuthors Info & Claims

IUI '16: Proceedings of the 21st International Conference on Intelligent User Interfaces

Pages 71 - 75

https://doi.org/10.1145/2856767.2856803

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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Cited By

Kotkov DMedlar ALiu YGlowacka DHui Yang GWang HHan SHauff CZuccon GZhang Y(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
https://dl.acm.org/doi/10.1145/3626772.3657735
Gönç KSağlam BDalmaz OÇukur TKozat SDibeklioglu H(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
https://dl.acm.org/doi/10.1145/3577190.3614137
Vuong TAndolina SJacucci GDaee PKlouche KSjöberg MRuotsalo TKaski S(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
https://dl.acm.org/doi/10.1145/3491101.3519910
Show More Cited By

Index Terms

Interactive Intent Modeling from Multiple Feedback Domains
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval

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Published In

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

General Chairs:
Jeffrey Nichols
Google Inc, USA
,
Jalal Mahmud
IBM Research, USA
,
John O'Donovan
UC Santa Barbara, USA
,
Program Chairs:
Cristina Conati
University of British Columbia, Canada
,
Massimo Zancanaro
Bruno Kessler Foundation, Italy

Copyright © 2016 ACM.

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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Publication History

Published: 07 March 2016

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Short-paper

Funding Sources

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

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IUI'16

Sponsor:

IUI'16: 21st International Conference on Intelligent User Interfaces

March 7 - 10, 2016

California, Sonoma, USA

Acceptance Rates

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

Kotkov DMedlar ALiu YGlowacka DHui Yang GWang HHan SHauff CZuccon GZhang Y(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
https://dl.acm.org/doi/10.1145/3626772.3657735
Gönç KSağlam BDalmaz OÇukur TKozat SDibeklioglu H(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
https://dl.acm.org/doi/10.1145/3577190.3614137
Vuong TAndolina SJacucci GDaee PKlouche KSjöberg MRuotsalo TKaski S(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
https://dl.acm.org/doi/10.1145/3491101.3519910
Vuong TAndolina SJacucci GDaee PKlouche KSjöberg MRuotsalo TKaski S(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
https://dl.acm.org/doi/10.1145/3460231.3478883
Kropotov IMedlar AGlowacka DDemartini GZuccon GCulpepper JHuang ZTong H(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
https://dl.acm.org/doi/10.1145/3459637.3482103
Jacucci GDaee PVuong TAndolina SKlouche KSjöberg MRuotsalo TKaski S(2021)Entity Recommendation for Everyday Digital TasksACM Transactions on Computer-Human Interaction10.1145/345891928:5(1-41)Online publication date: 20-Aug-2021
https://dl.acm.org/doi/10.1145/3458919
Barraza-Urbina AGlowacka D(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
https://dl.acm.org/doi/10.1145/3383313.3411547
Colella FDaee PJokinen JOulasvirta AKaski S(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
https://dl.acm.org/doi/10.1145/3340631.3394883
Glowacka DBogers TSaid ABrusilovsky PTikk D(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
https://dl.acm.org/doi/10.1145/3298689.3346956
Koch JLucero AHegemann LOulasvirta ABrewster SFitzpatrick GCox AKostakos V(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
https://dl.acm.org/doi/10.1145/3290605.3300863
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