QUARE: 1st Workshop on Measuring the Quality of Explanations in Recommender Systems
Authors: 
Alessandro Piscopo, Oana Inel, Sanne Vrijenhoek, Martijn Millecamp, 
Krisztian BalogAuthors Info & Claims
Alessandro Piscopo, Oana Inel, Sanne Vrijenhoek, Martijn Millecamp, Krisztian BalogAuthors Info & ClaimsPages 3478 - 3481
Abstract
QUARE - measuring the QUality of explAnations in REcommender systems - is the first workshop that aims to promote discussion upon future research and practice directions around evaluation methodologies for explanations in recommender systems. To that end, we bring together researchers and practitioners from academia and industry to facilitate discussions about the main issues and best practices in the respective areas, identify possible synergies, and outline priorities regarding future research directions. Additionally, we want to stimulate reflections around methods to systematically and holistically assess explanation approaches, impact, and goals, at the interplay between organisational and human values. The homepage of the workshop is available at: https://sites.google.com/view/quare-2022/.
References
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Krisztian Balog, Filip Radlinski, and Shushan Arakelyan. 2019. Transparent, Scrutable and Explainable User Models for Personalized Recommendation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '19). ACM, 265--274.
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Zuohui Fu, Yikun Xian, Ruoyuan Gao, Jieyu Zhao, Qiaoying Huang, Yingqiang Ge, Shuyuan Xu, Shijie Geng, Chirag Shah, Yongfeng Zhang, and Gerard de Melo. 2020. Fairness-Aware Explainable Recommendation over Knowledge Graphs. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '20). ACM, 69--78.
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Deepesh V. Hada, Vijaikumar M, and Shirish K. Shevade. 2021. ReXPlug: Explainable Recommendation using Plug-and-Play Language Model. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '21). ACM, 81--91.
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Chen He, Denis Parra, and Katrien Verbert. 2016. Interactive Recommender Systems: A survey of the State of the Art and Future Research Challenges and Opportunities. Expert Syst. Appl., Vol. 56 (2016), 9--27.
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Martijn Millecamp, Cristina Conati, and Katrien Verbert. 2022. "Knowing Me, Knowing You": Personalized Explanations for a Music Recommender System. User Model. User-adapt. Interact., Vol. 32 (2022), 215--252.
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Ingrid Nunes and Dietmar Jannach. 2017. A systematic review and taxonomy of explanations in decision support and recommender systems. User Model. User-adapt. Interact., Vol. 27, 3--5 (2017), 393--444.
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Khanh Hiep Tran, Azin Ghazimatin, and Rishiraj Saha Roy. 2021. Counterfactual Explanations for Neural Recommenders. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '21). ACM, 1627--1631.
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Index Terms
- QUARE: 1st Workshop on Measuring the Quality of Explanations in Recommender Systems
Recommendations
Report on the 1st Workshop on Measuring the Quality of Explanations in Recommender Systems (QUARE 2022) at SIGIR 2022
Explainable recommenders are systems that explain why an item is recommended, in addition to suggesting relevant items to the users of the system. Although explanations are known to be able to significantly affect a user's decision-making process, ...
QUARE: 2nd Workshop on Measuring the Quality of Explanations in Recommender Systems
RecSys '23: Proceedings of the 17th ACM Conference on Recommender SystemsQUARE1—measuring the QUality of explAnations in REcommender systems—is the second workshop which focuses on evaluation methodologies for explanations in recommender systems. We bring together researchers and practitioners from academia and industry to ...
A Counterfactual Framework for Learning and Evaluating Explanations for Recommender Systems
WWW '24: Proceedings of the ACM Web Conference 2024In the field of recommender systems, explainability remains a pivotal yet challenging aspect. To address this, we introduce the Learning to eXplain Recommendations (LXR) framework, a post-hoc, model-agnostic approach designed for providing counterfactual ...
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Information & Contributors
Information
Published In
July 2022
3569 pages
ISBN:9781450387323
DOI:10.1145/3477495
- General Chairs:
- Enrique Amigo,
- Pablo Castells,
- Julio Gonzalo,
- Program Chairs:
- Ben Carterette,
- J. Shane Culpepper,
- Gabriella Kazai
Copyright © 2022 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 07 July 2022
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SIGIR '22
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SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 11 - 15, 2022
Madrid, Spain
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Overall Acceptance Rate 792 of 3,983 submissions, 20%
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