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
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001) · Proceedings of the 10th international conference on World Wide Web · doi:10.1145/371920.372071 · Cited by 9,135
Approximation Algorithms for NP-Hard Problems
Hochba, D. S. (1997) · ACM SIGACT News · doi:10.1145/261342.571216 · Cited by 3,133
Improving recommendation lists through topic diversification
Ziegler, C.-N., McNee, S. M., Konstan, J. A., & Lausen, G. (2005) · International World Wide Web Conference (WWW) · doi:10.1145/1060745.1060754 · Cited by 1,920
Optimal aggregation algorithms for middleware
Fagin, R., Lotem, A., & Naor, M. (2003) · Journal of Computer and System Sciences · doi:10.1016/s0022-0000(03)00026-6 · Cited by 947
PolyLens: A Recommender System for Groups of Users
O’Connor, M., Cosley, D., Konstan, J. A., & Riedl, J. (2005) · Kluwer Academic Publishers eBooks · doi:10.1007/0-306-48019-0_11 · Cited by 498
Steihaus, H. (1948) · Econometrica · Cited by 440
Amer-Yahia, S., Roy, S. B., Chawlat, A., Das, G., & Yu, C. (2009) · Proceedings of the VLDB Endowment · doi:10.14778/1687627.1687713 · Cited by 411
Jameson, A., & Smyth, B. (2007) · Springer eBooks · doi:10.1007/978-3-540-72079-9_20 · Cited by 395
Supporting top-k join queries in relational databases
Ilyas, I. F., Aref, W. G., & Elmagarmid, A. K. (2004) · The VLDB Journal · doi:10.1007/s00778-004-0128-2 · Cited by 381
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Sorted by citations
Bias in data‐driven artificial intelligence systems—An introductory survey
Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M., Ruggieri, S., Turini, F., Papadopoulos, S., Krasanakis, E., et al. (2020) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1356 · Cited by 1,043
A Survey on the Fairness of Recommender Systems
Wang, Y., Ma, W., Zhang, M., Liu, Y., & Ma, S. (2022) · ACM Transactions on Information Systems · doi:10.1145/3547333 · Cited by 377
Fairness in recommender systems: research landscape and future directions
Deldjoo, Y., Jannach, D., Bellogín, A., Difonzo, A., & Zanzonelli, D. (2023) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-023-09364-z · Cited by 290
FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms
Patro, G. K., Biswas, A., Ganguly, N., Gummadi, K. P., & Chakraborty, A. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366423.3380196 · Cited by 204
Fairness in rankings and recommendations: an overview
Pitoura, E., Stefanidis, K., & Koutrika, G. (2021) · The VLDB Journal · doi:10.1007/s00778-021-00697-y · Cited by 177
Self-citation
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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