The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Fairness in Package-to-Group Recommendations
Open accessAbstract
Recommending packages of items to groups of users has several applications, including recommending vacation packages to groups of tourists, entertainment packages to groups of friends, or sets of courses to groups of students. In this paper, we focus on a novel aspect of package-to-group recommendations, that of fairness. Specifically, when we recommend a package to a group of people, we ask that this recommendation is fair in the sense that every group member is satisfied by a sufficient number of items in the package. We explore two definitions of fairness and show that for either definition the problem of finding the most fair package is NP-hard. We exploit the fact that our problem can be modeled as a coverage problem, and we propose greedy algorithms that find approximate solutions within reasonable time. In addition, we study two extensions of the problem, where we impose category or spatial constraints on the items to be included in the recommended packages. We evaluate the appropriateness of the fairness models and the performance of the proposed algorithms using real data from Yelp, and a user study.
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Cites
Adomavičius, G., & Tuzhilin, A. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.99 · Cited by 10,539
Item-based collaborative filtering recommendation algorithms
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Optimal aggregation algorithms for middleware
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Supporting top-k join queries in relational databases
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Mitigating Popularity Bias for Multi-view Group Recommendation
Geng, Q., Wang, X., Zhu, C., Chang, L., Zeng, Y., & Gan, Y. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-6963-9_21
Exploring Design Considerations for Group Recommendation Systems as a Social Catalyst
Kwak, D., Sohn, Y., & Lim, Y.-K. (2026) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3793676
Happiness Maximization Queries with Two-Sided Fairness
Dong, J., & Zheng, J. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-7394-3_40
Weighted Set Multi-Cover on Bounded Universe and Applications in Package Recommendation
Shahbazi, N., Esmailpour, A., & Sintos, S. (2026) · Proceedings of the ACM on Management of Data · doi:10.1145/3802126
An Optimization Framework for Balancing Accuracy and Fairness in Social-Choice Group Recommendation
Yera, R., Dutta, B., & Martínez, L. M. (2026) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai68641.2026.11536617
GroupRec: A Unified Toolkit for Reproducible and Inspectable Group Recommendation Research
Dokoupil, P., Boratto, L., & Peška, L. (2026) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3773078.3841268
The Taming of the Bias: Measuring and Mitigating Category-Aware Gender Bias in Recommendations
Kheya, T. A., Bouadjenek, M. R., & Aryal, S. (2026) · ACM Transactions on Recommender Systems · doi:10.1145/3849704
Pride in Precision, Prejudice in Prediction: Bias and Stereotyping in Recommendation Systems
Kheya, T. A. (2026) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3799682.3840226
Roy, F., Ding, X., Choo, K. K. R., & Zhou, P. (2024) · arXiv · doi:10.48550/arxiv.2409.12651 · Cited by 7
Metadata
- Authors
- Serbos, D., Qi, S., Mamoulis, N., Pitoura, E., & Tsaparas, P.
- Affiliations
- University of Ioannina (GR)
- University of Hong Kong (HK)
- Venue
- Proceedings of the 26th International Conference on World Wide Web
- Published
- 2017-04-03
- Pages
- 371-379
- OpenAlex
- W2604961496
- Cited by
- 107
- References
- 33
- Citations by year
- 2026: 7 · 2025: 5 · 2024: 11 · 2023: 20 · 2022: 16 · 2021: 18 · 2020: 13 · 2019: 10 · 2018: 5 · 2017: 2
- FWCI
- 6.30
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International World Wide Web Conference (WWW) (publishedVersion, cc-by)
- The HKU Scholars Hub (University of Hong Kong) (submittedVersion)
- Funders
- Hong Kong RGC (17205015)
- European Union (LBSKQ, JMUGCS)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Game Theory and Voting Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- group recommendation, fairness, covering problems, NP-hardness, greedy approximation algorithm, spatial constraints, user satisfaction
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