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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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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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