abstract

Preference Elicitation Strategy for Conversational Recommender System

Author:

Bilih PriyogiAuthors Info & Claims

WSDM '19: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

Pages 824 - 825

https://doi.org/10.1145/3289600.3291604

Published: 30 January 2019 Publication History

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Abstract

Traditionally, recommenders have been based on a single-shot model based on past user actions. Conversational recommenders allow incremental elicitation of user preference by performing user-system dialogue. For example, the systems can ask about user preference toward a feature associated with the items. In such systems, it is important to design an efficient conversation, which minimizes the number of question asked while maximizing the preference information obtained. Therefore, this research is intended to explore possible ways to design a conversational recommender with an efficient preference elicitation. Specifically, it focuses on the order of questions. Also, an idea proposed to suggest answers for each question asked, which can assist users in giving their feedback.

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Cited By

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Göpfert CHaig AHsu CChow YVendrov ILu TRamachandran DPham HGhavamzadeh MBoutilier C(2024)Discovering Personalized Semantics for Soft Attributes in Recommender Systems Using Concept Activation VectorsACM Transactions on Recommender Systems10.1145/36586752:4(1-37)Online publication date: 16-Apr-2024
https://dl.acm.org/doi/10.1145/3658675
Kalirai MKuzminykh A(2024)You Today, Better Tomorrow: Envisioning the Role of Conversation in Recommender Systems of the FutureProceedings of the 6th ACM Conference on Conversational User Interfaces10.1145/3640794.3665881(1-5)Online publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1145/3640794.3665881
Yu DLi QWang XLi QXu G(2024)Counterfactual Explainable Conversational RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.332240336:6(2388-2400)Online publication date: Jun-2024
https://doi.org/10.1109/TKDE.2023.3322403
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Index Terms

Preference Elicitation Strategy for Conversational Recommender System
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

WSDM '19: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

January 2019

874 pages

ISBN:9781450359405

DOI:10.1145/3289600

General Chairs:
J. Shane Culpepper
RMIT University
,
Alistair Moffat
The University of Melbourne
,
Program Chairs:
Paul N. Bennett
Microsoft
,
Kristina Lerman
University of Southern California

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 January 2019

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Funding Sources

LPDP (Indonesia Endowment Fund for Education)

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WSDM '19

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WSDM '19: The Twelfth ACM International Conference on Web Search and Data Mining

February 11 - 15, 2019

Melbourne VIC, Australia

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WSDM '19 Paper Acceptance Rate 84 of 511 submissions, 16%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

View all

Göpfert CHaig AHsu CChow YVendrov ILu TRamachandran DPham HGhavamzadeh MBoutilier C(2024)Discovering Personalized Semantics for Soft Attributes in Recommender Systems Using Concept Activation VectorsACM Transactions on Recommender Systems10.1145/36586752:4(1-37)Online publication date: 16-Apr-2024
https://dl.acm.org/doi/10.1145/3658675
Kalirai MKuzminykh A(2024)You Today, Better Tomorrow: Envisioning the Role of Conversation in Recommender Systems of the FutureProceedings of the 6th ACM Conference on Conversational User Interfaces10.1145/3640794.3665881(1-5)Online publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1145/3640794.3665881
Yu DLi QWang XLi QXu G(2024)Counterfactual Explainable Conversational RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.332240336:6(2388-2400)Online publication date: Jun-2024
https://doi.org/10.1109/TKDE.2023.3322403
Zhao SWei WLiu YWang ZLi WMao XZhu SYang MWen ZElkind E(2023)Towards hierarchical policy learning for conversational recommendation with hypergraph-based reinforcement learningProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/273(2459-2467)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/273
Jin YChen LCai WZhao X(2023)CRS-Que: A User-Centric Evaluation Framework for Conversational Recommender SystemsACM Transactions on Recommender Systems10.1145/3631534Online publication date: 2-Nov-2023
https://dl.acm.org/doi/10.1145/3631534
Shen QWu LZhang YPang YWei ZXu FLong BPei J(2023)Multi-Interest Multi-Round Conversational Recommendation System with Fuzzy Feedback Based User SimulatorACM Transactions on Recommender Systems10.1145/36163792:4(1-29)Online publication date: 24-Aug-2023
https://dl.acm.org/doi/10.1145/3616379
Xia YWu JYu TKim SRossi RLi SSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)User-Regulation Deconfounded Conversational Recommender System with Bandit FeedbackProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599539(2694-2704)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599539
Wang YChen XFang JMeng ZLiang S(2023)Enhancing Conversational Recommendation Systems with Representation FusionACM Transactions on the Web10.1145/357703417:1(1-34)Online publication date: 21-Feb-2023
https://dl.acm.org/doi/10.1145/3577034
Yilma BLeiva L(2023)The Elements of Visual Art RecommendationProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581477(1-17)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581477
Zhao SWei WMao XZhu SYang MWen ZChen DZhu FChen HDuh WHuang HKato MMothe JPoblete B(2023)Multi-view Hypergraph Contrastive Policy Learning for Conversational RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591737(654-664)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591737
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Recommendations

Improving preference elicitation in a conversational recommender system with active learning strategies

Conversational Recommender System

Using Groups of Items for Preference Elicitation in Recommender Systems