short-paper

HOOPS: Human-in-the-Loop Graph Reasoning for Conversational Recommendation

Authors:

Gerard de Melo,

Yongfeng ZhangAuthors Info & Claims

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 2415 - 2421

https://doi.org/10.1145/3404835.3463247

Published: 11 July 2021 Publication History

Abstract

There is increasing recognition of the need for human-centered AI that learns from human feedback. However, most current AI systems focus more on the model design, but less on human participation as part of the pipeline. In this work, we propose a Human-in-the-Loop (HitL) graph reasoning paradigm and develop a corresponding dataset named HOOPS for the task of KG-driven conversational recommendation. Specifically, we first construct a KG interpreting diverse user behaviors and identify pertinent attribute entities for each user--item pair. Then we simulate the conversational turns reflecting the human decision making process of choosing suitable items tracing the KG structures transparently. We also provide a benchmark method with reported performance on the dataset to ascertain the feasibility of HitL graph reasoning for recommendation using our developed dataset, and show that it provides novel opportunities for the research community.

References

[1]

Q. Ai, V. Azizi, X. Chen, and Y. Zhang. 2018. Learning heterogeneous knowledge base embeddings for explainable recommendation. Algorithms (2018).

[2]

S. Amershi, D. Weld, M. Vorvoreanu, A. Fourney, B. Nushi, P. Collisson, J. Suh, S. Iqbal, P. Bennett, K. Quinn, J. Teevan, R. Kikin-Gil, and E. Horvitz. 2019. Guidelines for Human-AI Interaction. CHI (2019).

[3]

Keping Bi, Qingyao Ai, Yongfeng Zhang, and W Bruce Croft. 2019. Conversational product search based on negative feedback. In CIKM .

[4]

A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. In NeurIPS .

[5]

P. Budzianowski, T. Wen, B. Tseng, I. Casanueva, S. Ultes, O. Ramadan, and M. Gasic. 2018. MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling. In EMNLP .

[6]

Hanxiong Chen, Shaoyun Shi, Yunqi Li, and Yongfeng Zhang. 2021. Neural Collaborative Reasoning. WWW (2021).

[7]

Q. Chen, J. Lin, Y. Zhang, M. Ding, Y. Cen, H. Yang, and J. Tang. 2019 b. Towards Knowledge-Based Recommender Dialog System. ArXiv 1908.05391 (2019).

[8]

Xu Chen, Hanxiong Chen, Hongteng Xu, Yongfeng Zhang, Yixin Cao, Zheng Qin, and Hongyuan Zha. 2019 a. Personalized fashion recommendation with visual explanations based on multimodal attention network: Towards visually explainable recommendation. In SIGIR .

[9]

Xu Chen, Yongfeng Zhang, and Zheng Qin. 2019 c. Dynamic explainable recommendation based on neural attentive models. In AAAI .

[10]

M. Eric, L. Krishnan, F. Charette, and C. D. Manning. 2017. Key-Value Retrieval Networks for Task-Oriented Dialogue. In SIGDIAL .

[11]

Zuohui Fu, Yikun Xian, Ruoyuan Gao, Jieyu Zhao, Qiaoying Huang, Yingqiang Ge, Shuyuan Xu, Shijie Geng, Chirag Shah, Yongfeng Zhang, et al. 2020 a. Fairness-aware explainable recommendation over knowledge graphs. In SIGIR. 69--78.

[12]

Zuohui Fu, Yikun Xian, Yongfeng Zhang, and Yi Zhang. 2020 b. Tutorial on Conversational Recommendation Systems. In RecSys .

[13]

Zuohui Fu, Yikun Xian, Yongfeng Zhang, and Yi Zhang. 2021 a. IUI 2021 Tutorial on Conversational Recommendation Systems. In IUI .

[14]

Zuohui Fu, Yikun Xian, Yongfeng Zhang, and Yi Zhang. 2021 b. WSDM 2021 Tutorial on Conversational Recommendation Systems. In WSDM .

[15]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR .

[16]

A. Jaimes, D. G. Perez, N. Sebe, and T. Huang. 2007. Guest Editors' Introduction: Human-Centered Computing--Toward a Human Revolution. Computer (2007).

[17]

R. Kling and S. L. Star. 1998. Human centered systems in the perspective of organizational and social informatics. SIGCAS Comput. Soc., Vol. 28 (1998), 22--29.

Digital Library

[18]

Wenqiang Lei, Gangyi Zhang, Xiangnan He, Yisong Miao, Xiang Wang, Liang Chen, and Tat-Seng Chua. 2020. Interactive Path Reasoning on Graph for Conversational Recommendation. In KDD .

[19]

Lei Li, Yongfeng Zhang, and Li Chen. 2020. Generate neural template explanations for recommendation. In CIKM .

[20]

Zelong Li, Jianchao Ji, Zuohui Fu, Yingqiang Ge, Shuyuan Xu, Chong Chen, and Yongfeng Zhang. 2021. Efficient Non-Sampling Knowledge Graph Embedding. WWW (2021).

[21]

Zhenyu Liao, Yikun Xian, Xiao Yang, Qinpei Zhao, Chenxi Zhang, and Jiangfeng Li. 2018. TSCSet: A crowdsourced time-sync comment dataset for exploration of user experience improvement. In IUI. 641--652.

[22]

Michael E. J. Masson. 1983. Conceptual processing of text during skimming and rapid sequential reading. Memory & Cognition, Vol. 11 (1983), 262--274.

[23]

T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean. 2013. Distributed representations of words and phrases and their compositionality. In NeurIPS .

[24]

Seungwhan Moon, Pararth Shah, Anuj Kumar, and Rajen Subba. 2019. OpenDialKG: Explainable conversational reasoning with attention-based walks over knowledge graphs. In ACL .

[25]

Mark Newman. 2010. Networks: An Introduction.

Digital Library

[26]

Jianmo Ni, Jiacheng Li, and Julian McAuley. 2019. Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects. In EMNLP .

[27]

Namyong Park, Andrey Kan, Xin Dong, Tong Ke Zhao, and Christos Faloutsos. 2019. Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks. KDD (2019).

[28]

Chen Qu, Liu Yang, Minghui Qiu, Yongfeng Zhang, Cen Chen, W Bruce Croft, and Mohit Iyyer. 2019. Attentive history selection for conversational question answering. In CIKM .

[29]

Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In UAI .

Digital Library

[30]

Shaoyun Shi, Hanxiong Chen, Weizhi Ma, Jiaxin Mao, Min Zhang, and Yongfeng Zhang. 2020. Neural Logic Reasoning. In CIKM. 1365--1374.

[31]

Yueming Sun and Yi Zhang. 2018. Conversational Recommender System. In SIGIR '18 .

[32]

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin. 2017. Attention is All you Need. In NIPS .

[33]

Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, and Minyi Guo. 2018. Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. In CIKM. ACM, 417--426.

Digital Library

[34]

Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, and Minyi Guo. 2019 b. Knowledge Graph Convolutional Networks for Recommender Systems. In WWW '19 .

[35]

Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua. 2019 a. KGAT: Knowledge Graph Attention Network for Recommendation. In KDD .

[36]

Sam Wiseman, Stuart M. Shieber, and Alexander M. Rush. 2018. Learning Neural Templates for Text Generation. In EMNLP .

[37]

Yikun Xian, Zuohui Fu, Qiaoying Huang, Shan Muthukrishnan, and Yongfeng Zhang. 2020 a. Neural-Symbolic Reasoning over Knowledge Graph for Multi-Stage Explainable Recommendation. arXiv preprint arXiv:2007.13207 (2020).

[38]

Yikun Xian, Zuohui Fu, S. Muthukrishnan, Gerard de Melo, and Yongfeng Zhang. 2019. Reinforcement Knowledge Graph Reasoning for Explainable Recommendation. SIGIR (2019).

[39]

Yikun Xian, Zuohui Fu, Handong Zhao, Yingqiang Ge, Xu Chen, Qiaoying Huang, Shijie Geng, Zhou Qin, Gerard De Melo, Shan Muthukrishnan, et al. 2020 b. CAFE: Coarse-to-fine neural symbolic reasoning for explainable recommendation. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 1645--1654.

Digital Library

[40]

L. Yang, M. Qiu, C. Qu, J. Guo, Y. Zhang, W. B. Croft, J. Huang, and H. Chen. 2018b. Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems. SIGIR (2018).

[41]

Qian Yang, Nikola Banovic, and John Zimmerman. 2018a. Mapping Machine Learning Advances from HCI Research to Reveal Starting Places for Design Innovation. CHI (2018).

[42]

Chunyuan Yuan, Wen jie Zhou, MingMing Li, Shangwen Lv, Fuqing Zhu, Jizhong Han, and Songlin Hu. 2019. Multi-hop Selector Network for Multi-turn Response Selection in Retrieval-based Chatbots. In EMNLP/IJCNLP .

[43]

Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma. 2016. Collaborative Knowledge Base Embedding for Recommender Systems. In KDD .

[44]

Yongfeng Zhang and Xu Chen. 2018. Explainable Recommendation: A Survey and New Perspectives. Found. Trends Inf. Retr., Vol. 14 (2018), 1--101.

Digital Library

[45]

Yongfeng Zhang, Xu Chen, Qingyao Ai, Liu Yang, and W Bruce Croft. 2018. Towards conversational search and recommendation: System ask, user respond. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, 177--186.

Digital Library

[46]

Yongfeng Zhang, Guokun Lai, Min Zhang, Yi Zhang, Yiqun Liu, and Shaoping Ma. 2014. Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval. ACM, 83--92.

Digital Library

[47]

Wayne Xin Zhao, Gaole He, Kunlin Yang, Hongjian Dou, Jin Huang, Siqi Ouyang, and Ji-Rong Wen. 2019. KB4Rec: A Data Set for Linking Knowledge Bases with Recommender Systems. Data Intelligence, Vol. 1, 2 (2019), 121--136.

[48]

Xiangyang Zhou, Lu Li, Daxiang Dong, Yi Liu, Ying Chen, Wayne Xin Zhao, Dianhai Yu, and Hua Wu. 2018. Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network. In ACL .

[49]

Yaxin Zhu, Yikun Xian, Zuohui Fu, Gerard de Melo, and Yongfeng Zhang. 2021. Faithfully Explainable Recommendation via Neural Logic Reasoning. NAACL (2021).

Cited By

Wang HYu ZZhang YWang YYang FWang LLiu JGuo B(2024)hmOS: An Extensible Platform for Task-Oriented Human–Machine ComputingIEEE Transactions on Human-Machine Systems10.1109/THMS.2024.341443254:5(536-545)Online publication date: Oct-2024
https://doi.org/10.1109/THMS.2024.3414432
Schmager SGrøder CParmiggiani EPappas IVassilakopoulou P(2024)Exploring citizens’ stances on AI in public services: A social contract perspectiveData & Policy10.1017/dap.2024.136Online publication date: 22-Mar-2024
https://doi.org/10.1017/dap.2024.13
Haruyama MHidaka K(2024)What influences users to provide explicit feedback? A case of food delivery recommendersUser Modeling and User-Adapted Interaction10.1007/s11257-023-09385-834:3(753-796)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s11257-023-09385-8
Show More Cited By

Index Terms

HOOPS: Human-in-the-Loop Graph Reasoning for Conversational Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

Recommendations

Interactive Path Reasoning on Graph for Conversational Recommendation
KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Traditional recommendation systems estimate user preference on items from past interaction history, thus suffering from the limitations of obtaining fine-grained and dynamic user preference. Conversational recommendation system (CRS) brings revolutions ...
Conversational Collaborative Recommendation --- An Experimental Analysis

Traditionally, collaborative recommender systems have been based on a single-shot model of recommendation where a single set of recommendations is generated based on a user's (past) stored preferences. However, content-based recommender system research ...
A Tag-Based Post-Hoc Framework for Explainable Conversational Recommendation
ICTIR '22: Proceedings of the 2022 ACM SIGIR International Conference on Theory of Information Retrieval

Explanations are important for conversational recommendation. They help users to understand how the recommender system works, and elicit user's responses by allowing users to provide informative feedback on them. During the interaction with users, ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2021

2998 pages

ISBN:9781450380379

DOI:10.1145/3404835

General Chairs:
Fernando Diaz
(Google)
,
Chirag Shah
University of Washington
,
Torsten Suel
New York University
,
Program Chairs:
Pablo Castells
Universidad Autónoma de Madrid, Amazon
,
Rosie Jones
Spotify
,
Tetsuya Sakai
Waseda University

Copyright © 2021 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 the author(s) 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].

Sponsors

SIGIR: ACM Special Interest Group on Information Retrieval

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 July 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Funding Sources

NSF

Conference

SIGIR '21

Sponsor:

SIGIR

SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2021

Virtual Event, Canada

Acceptance Rates

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

18
Total Citations
View Citations
1,251
Total Downloads

Downloads (Last 12 months)169
Downloads (Last 6 weeks)8

Reflects downloads up to 16 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Wang HYu ZZhang YWang YYang FWang LLiu JGuo B(2024)hmOS: An Extensible Platform for Task-Oriented Human–Machine ComputingIEEE Transactions on Human-Machine Systems10.1109/THMS.2024.341443254:5(536-545)Online publication date: Oct-2024
https://doi.org/10.1109/THMS.2024.3414432
Schmager SGrøder CParmiggiani EPappas IVassilakopoulou P(2024)Exploring citizens’ stances on AI in public services: A social contract perspectiveData & Policy10.1017/dap.2024.136Online publication date: 22-Mar-2024
https://doi.org/10.1017/dap.2024.13
Haruyama MHidaka K(2024)What influences users to provide explicit feedback? A case of food delivery recommendersUser Modeling and User-Adapted Interaction10.1007/s11257-023-09385-834:3(753-796)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s11257-023-09385-8
Deng YZhang WXu WLei WChua TLam W(2023)A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender SystemsACM Transactions on Information Systems10.1145/357064041:3(1-25)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1145/3570640
Liu YZhang WDong BFan YWang HFeng FChen YZhuang ZCui HLi YChe WChen HDuh WHuang HKato MMothe JPoblete B(2023)U-NEED: A Fine-grained Dataset for User Needs-Centric E-commerce Conversational RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591878(2723-2732)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591878
Qu XLiu HSun ZYin XOng YLu LMa ZChen HDuh WHuang HKato MMothe JPoblete B(2023)Towards Building Voice-based Conversational Recommender Systems: Datasets, Potential Solutions and ProspectsProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591876(2701-2711)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591876
Solovyev NBarron RBhattarai MEren MRasmussen KAlexandrov B(2023)Interactive Distillation of Large Single-Topic Corpora of Scientific Papers2023 International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA58977.2023.00148(1000-1005)Online publication date: 15-Dec-2023
https://doi.org/10.1109/ICMLA58977.2023.00148
Zhang LLiu PGulla J(2023)Recommending on graphs: a comprehensive review from a data perspectiveUser Modeling and User-Adapted Interaction10.1007/s11257-023-09359-w33:4(803-888)Online publication date: 13-Mar-2023
https://dl.acm.org/doi/10.1007/s11257-023-09359-w
Viola Mde Queiroz DMotz R(2023)Is the Human-in-the-Loop Concept Applied in Educational Recommender Systems?Perspectives and Trends in Education and Technology10.1007/978-981-99-5414-8_61(667-676)Online publication date: 22-Oct-2023
https://doi.org/10.1007/978-981-99-5414-8_61
Fang HChen CLong YXu GXiao Y(2022)DTCRSKG: A Deep Travel Conversational Recommender System Incorporating Knowledge GraphMathematics10.3390/math1009140210:9(1402)Online publication date: 22-Apr-2022
https://doi.org/10.3390/math10091402
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents