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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Yuan, Q., Cong, G., & Lin, C.-Y. (2014) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2623330.2623616 · Cited by 203
Maximum Coverage Problem with Group Budget Constraints and Applications
Chekuri, C., & Kumar, A. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-27821-4_7 · Cited by 196
Recommendation systems with complex constraints
Parameswaran, A., Venetis, P., & García-Molina, H. (2011) · ACM Transactions on Information Systems · doi:10.1145/2037661.2037665 · Cited by 153
Exploring personal impact for group recommendation
Liu, X., Tian, Y., Ye, M., & Lee, W.-C. (2012) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2396761.2396848 · Cited by 149
Selecting a comprehensive set of reviews
Tsaparas, P., Ntoulas, A., & Terzi, E. (2011) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2020408.2020440 · Cited by 124
Self-citationBundle recommendation in ecommerce
Zhu, T., Harrington, P., Junjun, L., & Tang, L. (2014) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2600428.2609603 · Cited by 117
Breaking out of the box of recommendations
Xie, M., Lakshmanan, L. V. S., & Wood, P. T. (2010) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/1864708.1864739 · Cited by 113
Intent-oriented diversity in recommender systems
Vargas, S., Castells, P., & Vallet, D. (2011) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2009916.2010124 · Cited by 97
Probabilistic group recommendation via information matching
Gorla, J., Lathia, N., Robertson, S. E., & Wang, Y.-X. (2013) · International World Wide Web Conference (WWW) · doi:10.1145/2488388.2488432 · Cited by 92
Personality aware recommendations to groups
Recio-García, J. A., Jiménez-Díaz, G., Sánchez‐Ruiz, A. A., & Díaz-Agudo, B. (2009) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/1639714.1639779 · Cited by 89
Li, K., Lu, W., Bhagat, S., Lakshmanan, L. V. S., & Yu, C. (2014) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2623330.2623724 · Cited by 79
A fast approximation algorithm for the multicovering problem
Hall, N. G., & Hochbaum, D. S. (1986) · Discrete Applied Mathematics · doi:10.1016/0166-218x(86)90016-8 · Cited by 74
Envy Freeness in Experimental Fair Division Problems
Herreiner, D. K., & Puppe, C. (2007) · Theory and Decision · doi:10.1007/s11238-007-9069-8 · Cited by 47
Composite Retrieval of Diverse and Complementary Bundles
Amer-Yahia, S., Bonchi, F., Castillo, C., Feuerstein, E., Mendez-Diaz, I., & Zabala, P. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2014.2306678 · Cited by 42
Counteracting Anchoring Effects in Group Decision Making
Stettinger, M., Felfernig, A., Leitner, G., & Reiterer, S. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-20267-9_10 · Cited by 40
Recommending Packages to Groups
Qi, S., Mamoulis, N., Pitoura, E., & Tsaparas, P. (2016) · Proceedings · doi:10.1109/icdm.2016.0056 · Cited by 39
Self-citationExact Algorithms for Set Multicover and Multiset Multicover Problems
Hua, Q.-S., Yu, D., Lau, F. C. M., & Wang, Y. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-10631-6_6 · Cited by 31
Stettinger, M. (2014) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/2645710.2653365 · Cited by 30
Exploiting group recommendation functions for flexible preferences
Roy, S. B., Thirumuruganathan, S., Amer-Yahia, S., Das, G., & Yu, C. (2014) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2014.6816669 · Cited by 28
Generating top-k packages via preference elicitation
Xie, M., Lakshmanan, L. V. S., & Wood, P. T. (2014) · Proceedings of the VLDB Endowment · doi:10.14778/2733085.2733099 · Cited by 28
On the Complexity of Package Recommendation Problems
Deng, T., Fan, W., & Geerts, F. (2013) · SIAM Journal on Computing · doi:10.1137/120904524 · Cited by 19
A Versatile Graph-Based Approach to Package Recommendation
Interdonato, R., Romeo, S. O., Tagarelli, A., & Karypis, G. (2013) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2013.130 · Cited by 18
Set multi-covering via inclusion–exclusion
Hua, Q.-S., Wang, Y., Yu, D., & Lau, F. C. M. (2009) · Theoretical Computer Science · doi:10.1016/j.tcs.2009.05.020 · Cited by 16
Cited by
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
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 ›
Are you an author, or do you see a problem with this page? Request a review
