Enumerating Fair Packages for Group Recommendations

@article{Sato2022EnumeratingFP,
  title={Enumerating Fair Packages for Group Recommendations},
  author={R. Sato},
  journal={Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining},
  year={2022}
}
  • R. Sato
  • Published 30 May 2021
  • Computer Science
  • Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
Package-to-group recommender systems recommend a set of unified items to a group of people. Different from conventional settings, it is not easy to measure the utility of group recommendations because it involves more than one user. In particular, fairness is crucial in group recommendations. Even if some members in a group are substantially satisfied with a recommendation, it is undesirable if other members are ignored to increase the total utility. Many methods for evaluating and applying the… 

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References

SHOWING 1-10 OF 56 REFERENCES
Ensuring Fairness in Group Recommendations by Rank-Sensitive Balancing of Relevance
TLDR
A definition of fairness that ‘balances’ the relevance of the recommended items across the group members in a rank-sensitive way is given and a greedy algorithm (GFAR) is provided for finding a top-N set of group recommendations that satisfies this definition.
Fairness in Package-to-Group Recommendations
TLDR
A novel aspect of package-to-group recommendations, that of fairness, is focused on and two definitions of fairness are explored, showing that for either definition the problem of finding the most fair package is NP-hard.
Efficiency and envy-freeness in fair division of indivisible goods: logical representation and complexity
TLDR
This work considers the problem of allocating fairly a set of indivisible goods among agents from the point of view of compact representation and computational complexity, and identifies the complexity of determining whether there exists an efficient and envy-free allocation.
Recommending packages to groups. In ICDM, pages 449–458
  • IEEE Computer Society,
  • 2016
Fairness-Aware Group Recommendation with Pareto-Efficiency
TLDR
This work provides an optimization framework for fairness-aware group recommendation from the perspective of Pareto Efficiency and proposes two concepts of social welfare and fairness for modeling the overall utilities and the balance between group members.
Bundle recommendation in ecommerce
TLDR
By solving the BRP, this paper is able to find the optimal bundle of items to recommend with respect to preferred business objective and shows that it may be sufficient to solve a significantly smaller version of BRP depending on properties of input data.
Zero-Suppressed BDDs for Set Manipulation in Combinatorial Problems
  • S. Minato
  • Computer Science
    30th ACM/IEEE Design Automation Conference
  • 1993
TLDR
Using 0-Sup-BDDs, this data structure brings unique and compact representation of sets which appear in many combinatorial problems and can manipulate such sets more simply and efficiently than using original BDDs.
The Art of Computer Programming, Volume 4, Fascicle 1: Bitwise Tricks & Techniques; Binary Decision Diagrams
  • Addison-Wesley Professional,
  • 2009
Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data?
TLDR
This work proposes methods to enable the users to build their own fair recommender systems and empirically validate that the proposed method improves fairness substantially without harming much performance of the original unfair system.
InFoRM: Individual Fairness on Graph Mining
TLDR
This paper presents the first principled study of Individual Fairness on gRaph Mining (InFoRM), and presents a generic definition of individual fairness for graph mining which naturally leads to a quantitative measure of the potential bias in graph mining results.
...
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