research-article

ReDBot: Exploring Conversational Recommendation for Decision-Making Support in Group Chats

Authors:

Zhenhui PengAuthors Info & Claims

CHCHI '23: Proceedings of the Eleventh International Symposium of Chinese CHI

Pages 73 - 80

https://doi.org/10.1145/3629606.3629615

Published: 27 February 2024 Publication History

Abstract

Group Decision-Making (GDM) commonly takes place online, e.g., in text-based group chats, for daily tasks like choosing a movie or a restaurant. However, reaching a consensus among members in GDM tasks online is non-trivial due to the high workload of collecting necessary information and low awareness of group preferences. In this paper, we explore the design and impact of conversational recommendation for GDM support. Inspired by theories of GDM, we propose a ReDBot that asks questions to identify the group preference and recommends alternatives that match the group preference. We power ReDBot with recent large language models to handle the conversational flow. Our preliminary user study with four three-member groups suggests that ReDBot could reduce members’ workload in collecting information, improve awareness of group preferences, and boost consensus-reaching in GDM group chats. We conclude with design considerations for GDM support.

References

[1]

Sergio Alonso, Enrique Herrera-Viedma, Francisco Javier Cabrerizo, Carlos Porcel, and Antonio Gabriel López-Herrera. 2007. Using visualization tools to guide consensus in group decision making. In Applications of Fuzzy Sets Theory: 7th International Workshop on Fuzzy Logic and Applications, WILF 2007, Camogli, Italy, July 7-10, 2007. Proceedings 7. Springer, 77–85.

Digital Library

[2]

Sergio Alonso, Enrique Herrera-Viedma, Francisco Chiclana, and Francisco Herrera. 2010. A web based consensus support system for group decision making problems and incomplete preferences. Information Sciences 180, 23 (2010), 4477–4495.

Digital Library

[3]

Jesús Omar Álvarez Márquez and Jürgen Ziegler. 2016. Hootle+: A group recommender system supporting preference negotiation. In Collaboration and Technology: 22nd International Conference, CRIWG 2016, Kanazawa, Japan, September 14-16, 2016, Proceedings 22. Springer, 151–166.

[4]

Anne Aula, Natalie Jhaveri, and Mika Käki. 2005. Information search and re-access strategies of experienced web users. In Proceedings of the 14th international conference on World Wide Web. 583–592.

Digital Library

[5]

Sandeep Avula, Jaime Arguello, Robert Capra, Jordan Dodson, Yuhui Huang, and Filip Radlinski. 2019. Embedding search into a conversational platform to support collaborative search. In Proceedings of the 2019 Conference on Human Information Interaction and Retrieval. 15–23.

Digital Library

[6]

Sandeep Avula, Gordon Chadwick, Jaime Arguello, and Robert Capra. 2018. Searchbots: User engagement with chatbots during collaborative search. In Proceedings of the 2018 conference on human information interaction & retrieval. 52–61.

Digital Library

[7]

Valerie Belton and Theodor Stewart. 2002. Multiple criteria decision analysis: an integrated approach. Springer Science & Business Media.

[8]

Robert Capra, Annie T Chen, Katie Hawthorne, Jaime Arguello, Lee Shaw, and Gary Marchionini. 2012. Design and evaluation of a system to support collaborative search. Proceedings of the American Society for Information Science and Technology 49, 1 (2012), 1–10.

[9]

Robert Capra, Javier Velasco-Martin, and Beth Sams. 2011. Collaborative information seeking by the numbers. In Proceedings of the 3rd international workshop on Collaborative information retrieval. 7–10.

Digital Library

[10]

João Carneiro, Patrícia Alves, Goreti Marreiros, and Paulo Novais. 2021. Group decision support systems for current times: Overcoming the challenges of dispersed group decision-making. Neurocomputing 423 (2021), 735–746.

[11]

Konstantina Christakopoulou, Filip Radlinski, and Katja Hofmann. 2016. Towards conversational recommender systems. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 815–824.

Digital Library

[12]

Frank Cullen. 2005. Factors influencing restaurant selection in Dublin. Journal of Foodservice Business Research 7, 2 (2005), 53–85.

[13]

Alexander Felfernig, Nava Tintarev, TNT Trang, and Martin Stettinger. 2021. Designing explanations for group recommender systems. arXiv preprint arXiv:2102.12413 (2021).

[14]

Donelson R Forsyth. 2018. Group dynamics. Cengage Learning.

[15]

Fiona E Fox, Marianne Morris, and Nichola Rumsey. 2007. Doing synchronous online focus groups with young people: Methodological reflections. Qualitative health research 17, 4 (2007), 539–547.

[16]

Gene Golovchinsky, Abdigani Diriye, and Jeremy Pickens. 2011. Designing for collaboration in information seeking. Proc. HCIR (2011).

[17]

Emily Hindalong, Jordon Johnson, Giuseppe Carenini, and Tamara Munzner. 2020. Towards Rigorously Designed Preference Visualizations for Group Decision Making. In 2020 IEEE Pacific Visualization Symposium (PacificVis). IEEE, 181–190.

[18]

Sungsoo Hong, Minhyang Suh, Nathalie Henry Riche, Jooyoung Lee, Juho Kim, and Mark Zachry. 2018. Collaborative dynamic queries: Supporting distributed small group decision-making. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 1–12.

Digital Library

[19]

Dietmar Jannach, Ahtsham Manzoor, Wanling Cai, and Li Chen. 2021. A survey on conversational recommender systems. ACM Computing Surveys (CSUR) 54, 5 (2021), 1–36.

Digital Library

[20]

Janusz Kacprzyk and Sławomir Zadrożny. 2010. Supporting consensus reaching processes under fuzzy preferences and a fuzzy majority via linguistic summaries. Preferences and Decisions: Models and Applications (2010), 261–279.

[21]

Soomin Kim, Jinsu Eun, Changhoon Oh, Bongwon Suh, and Joonhwan Lee. 2020. Bot in the bunch: Facilitating group chat discussion by improving efficiency and participation with a chatbot. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–13.

Digital Library

[22]

Soomin Kim, Jinsu Eun, Joseph Seering, and Joonhwan Lee. 2021. Moderator chatbot for deliberative discussion: Effects of discussion structure and discussant facilitation. Proceedings of the ACM on Human-Computer Interaction 5, CSCW1 (2021), 1–26.

Digital Library

[23]

Pei Liu and Eliza Ching-Yick Tse. 2018. Exploring factors on customers’ restaurant choice: an analysis of restaurant attributes. British Food Journal 120, 10 (2018), 2289–2303.

[24]

Meredith Ringel Morris. 2013. Collaborative search revisited. In Proceedings of the 2013 conference on Computer supported cooperative work. 1181–1192.

Digital Library

[25]

Meredith Ringel Morris and Eric Horvitz. 2007. SearchTogether: an interface for collaborative web search. In Proceedings of the 20th annual ACM symposium on User interface software and technology. 3–12.

Digital Library

[26]

Bonnie A Nardi, Steve Whittaker, and Erin Bradner. 2000. Interaction and outeraction: Instant messaging in action. In Proceedings of the 2000 ACM conference on Computer supported cooperative work. 79–88.

Digital Library

[27]

Iván Palomares, Francisco J Estrella, Luis Martínez, and Francisco Herrera. 2014. Consensus under a fuzzy context: Taxonomy, analysis framework AFRYCA and experimental case of study. Information Fusion 20 (2014), 252–271.

[28]

Zhenhui Peng, Taewook Kim, and Xiaojuan Ma. 2019. GremoBot: Exploring emotion regulation in group chat. In Conference Companion Publication of the 2019 on Computer Supported Cooperative Work and Social Computing. 335–340.

Digital Library

[29]

Yueming Sun and Yi Zhang. 2018. Conversational recommender system. In The 41st international acm sigir conference on research & development in information retrieval. 235–244.

[30]

Dakuo Wang, Haoyu Wang, Mo Yu, Zahra Ashktorab, and Ming Tan. 2022. Group chat ecology in enterprise instant messaging: How employees collaborate through multi-user chat channels on slack. Proceedings of the ACM on Human-Computer Interaction 6, CSCW1 (2022), 1–14.

Digital Library

[31]

Zhen Yue, Shuguang Han, and Daqing He. 2012. An investigation of search processes in collaborative exploratory web search. Proceedings of the American Society for Information Science and Technology 49, 1 (2012), 1–4.

[32]

Cristina Zuheros, Eugenio Martínez-Cámara, Enrique Herrera-Viedma, and Francisco Herrera. 2021. Sentiment analysis based multi-person multi-criteria decision making methodology using natural language processing and deep learning for smarter decision aid. Case study of restaurant choice using TripAdvisor reviews. Information Fusion 68 (2021), 22–36.

Cited By

Fan HChen GWang XPeng Z(2024)LessonPlanner: Assisting Novice Teachers to Prepare Pedagogy-Driven Lesson Plans with Large Language ModelsProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676390(1-20)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3654777.3676390

Index Terms

ReDBot: Exploring Conversational Recommendation for Decision-Making Support in Group Chats
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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CHCHI '23: Proceedings of the Eleventh International Symposium of Chinese CHI

November 2023

634 pages

ISBN:9798400716454

DOI:10.1145/3629606

Copyright © 2023 ACM.

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Association for Computing Machinery

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Publication History

Published: 27 February 2024

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Natural Science Foundation of China

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CHCHI 2023

CHCHI 2023: Chinese CHI 2023

November 13 - 16, 2023

Denpasar, Bali, Indonesia

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Overall Acceptance Rate 17 of 40 submissions, 43%

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Cited By

Fan HChen GWang XPeng Z(2024)LessonPlanner: Assisting Novice Teachers to Prepare Pedagogy-Driven Lesson Plans with Large Language ModelsProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676390(1-20)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3654777.3676390

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