Pith. sign in

REVIEW 3 major objections 5 minor 51 references

Understanding Accuracy-Fairness Trade-offs in Re-ranking through Elasticity in Economics

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read One economic law governs the fairness-accuracy trade-off in ranking

desk verdict A useful evaluation curve and a fast heuristic, wrapped in an economic analogy that doesn't survive contact with its own equations. read the letter →

arxiv 2504.14991 v1 pith:4WR7NJZO submitted 2025-04-21 cs.IR

Chen Xu , Jujia Zhao , Wenjie Wang , Liang Pang , Jun Xu , Tat-Seng Chua , Maarten de Rijke This is my paper · ORCID
classification cs.IR
keywords re-rankingfairnesselasticitycommoditytaxtransferfairrankingevaluationEF-CurveElasticRankaccuracy-fairnesstrade-off
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that the familiar trade-off between ranking accuracy and item fairness is not a collection of unrelated engineering compromises but a single economic mechanism: imposing fairness constraints on item groups acts like a commodity tax on suppliers, and part of that tax is inevitably passed on to users as accuracy loss. The extent of the pass-through, the paper argues, is governed by the elasticity of utility between item groups, a quantity that is pinned down by a one-parameter family of fairness metrics. On this basis the authors propose the Elastic Fairness Curve (EF-Curve) as an evaluation framework that locates every fairness metric on a spectrum of tax rates, and ElasticRank, a re-ranking algorithm that re-weights items by the curved-space distance computed from that elasticity. If the account is right, then choosing a fairness metric is equivalent to choosing where on the elasticity spectrum to operate, and ElasticRank provides a fast way to move along that spectrum under a user-specified accuracy budget.

What carries the argument

The central object is the parametric fairness metric $f(\boldsymbol{v};t) = \operatorname{sign}(1-t)(\sum_g \bar{v}_g^{1-t})^{1/t}$, whose tax-base parameter $t$ indexes the whole family of fairness metrics ($t\to 0$ recovers entropy fairness, $t\to\infty$ recovers max-min fairness, and other values give proportional fairness, $\alpha$-fairness, p-norm, and related metrics). The elasticity identity $E_{r,p} = (\bar{v}_r/\bar{v}_p)^{-|t|}$ links this family to the economic notion of a commodity tax: it gives the rate at which a rich group is taxed relative to a poor group when the next item is added to the ranked list. The load-bearing equation is the transfer ratio $\gamma = 1 - 1/(1+k(E_{r,p}))$, which expresses the accuracy loss per unit of fairness gain as a function of these elasticities. ElasticRank operationalizes the same idea by replacing the flat ranking score with $s_{u,i} + d(g(i), a)$, where $d$ is the arc-length distance in the utility space curved by the elasticity; this distance is computed in closed form and adds no asymptotic complexity beyond standard ranking.

What would settle it

Numeric optimization on small synthetic instances can settle the theorem: find a utility vector $\boldsymbol{v}$ and accuracy function $a(\boldsymbol{w})$ for which the argmax of $\sum_g v_g + \lambda f(\boldsymbol{v})$ differs from the argmax of $f(\boldsymbol{v};|t|)^{|t|} a(\boldsymbol{w})^{1-|t|}$; such a counterexample would refute Theorem 3 and undercut the claim that the trade-off is governed solely by $E_{r,p}$. A second check is to measure the ratio $\partial v_p/\partial v_r$ directly from logged exposure and utility data and compare it with $(\bar{v}_r/\bar{v}_p)^{-|t|}$ predicted by Theorem 2.

Watch

Extended reading notes

Core claim

In the paper's own terms, the accuracy-fairness trade-off in re-ranking is governed by the elasticity of utility between item groups. The authors define the fairness objective through the family $f(\boldsymbol{v};t) = \operatorname{sign}(1-t)\left(\sum_{g} \bar{v}_g^{1-t}\right)^{1/t}$, and show that every common fairness metric (entropy, max-min, $\alpha$-fairness, p-norm, Jain's index) corresponds to a particular value of the tax-base parameter $t$. The elasticity between a rich group $r$ and a poor group $p$ is $E_{r,p} = \partial v_p/\partial v_r = (\bar{v}_r/\bar{v}_p)^{-|t|}$, so different fairness metrics impose different relative commodity taxes on rich and poor groups. The trade-off itself is then characterized by a transfer ratio $\gamma = 1 - 1/(1+k(E_{r,p}))$ that measures how much of the fairness tax is shifted onto users as accuracy loss. The EF-Curve, plotting $f(\boldsymbol{v};t)$ against $t$, is proposed as a comprehensive evaluation, and ElasticRank, which adds to each item's score a curved-space distance $d(g(i), a) \approx (1-t)v_{g(i)}^{-t}(v_a^{1-t} - v_{g(i)}^{1-t})$, is designed to optimize the area under that curve. Experiments on Steam, Amazon-Digital-Music, and Yelp report that ElasticRank dominates the baselines on the accuracy-fairness Pareto front and runs faster than the optimization-based baselines.

Load-bearing premise

The proof that the linear accuracy-plus-fairness objective in Eq. (6) can be replaced by the product objective $L = f(\boldsymbol{v};|t|)^{|t|} a(\boldsymbol{w})^{1-|t|}$ without changing its optimum rests only on continuity and convexity of the feasible region, which do not by themselves guarantee identical argmax; if that equivalence fails, the elasticity-based account of the trade-off has no quantitative support.

Editorial extensions

If this is right

  • All fairness metrics used in ranking are points on a single elasticity spectrum parameterized by $t$; choosing a metric is equivalent to choosing a tax rate.
  • The EF-Curve allows a direct visual comparison of algorithms across the whole fairness spectrum, and the enclosed area $EF$ gives a single quantitative performance score.
  • ElasticRank is reported to achieve Pareto-dominant accuracy-fairness trade-offs on Steam, Amazon-Digital-Music, and Yelp when accuracy is held near 99% NDCG.
  • ElasticRank's inference cost is on par with standard sorting, and in the reported experiments it is about an order of magnitude faster than Welf and two orders of magnitude faster than P-MMF.
  • Because the transfer ratio depends on elasticity, effective fairness interventions should shift exposure from high-elasticity (luxury-like) groups to low-elasticity (necessity-like) groups to minimize the accuracy cost passed to users.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the elasticity account is correct, a platform's choice of fairness metric is also a prediction about which item groups are 'necessities' and which are 'luxuries' to its users; the same metric could be revisited when user behavior changes.
  • A direct empirical test would estimate the actual elasticity $\partial v_p/\partial v_r$ from logged exposure and utility data and compare it with the closed form $(\bar{v}_r/\bar{v}_p)^{-|t|}$; divergence would indicate that the EF-Curve is a normative construction rather than a descriptive law.
  • The economic analogy suggests that introducing 'savings' — outside options or substitute items users can turn to — could lower the accuracy cost of fairness in a dynamic setting, since users would have alternatives when exposure is redistributed.
  • The EF-Curve could be used as an algorithm-independent diagnostic for any fairness intervention, plotting which groups are supported and which are restricted, even outside re-ranking tasks such as ads or search.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper argues that the accuracy--fairness trade-off in re-ranking is governed by an economic notion of elasticity. It defines a parametric family of fairness metrics f(v;t), uses it to build an evaluation curve (EF-Curve) and area metric (EF), and proposes ElasticRank, a re-ranking algorithm that adjusts group distances according to a 'curved' elasticity-based score. Experiments on Steam, Amazon Digital Music, and Yelp report that ElasticRank matches the accuracy of strong baselines near 99% NDCG while achieving higher EF and lower inference time.

Significance. If the theoretical derivation were correct, the paper would provide a unifying view of fairness metrics and a practical algorithm with a clear selection criterion, which would be valuable for the fair-ranking community. The paper has concrete strengths: it releases code, evaluates on three public datasets, and presents a clearly motivated framework. However, the central elasticity mechanism is not established: the key identity in Theorem 2 does not follow from the stated definitions, and the equivalence in Theorem 3 is false as stated. Because the framework, the EF metric, and ElasticRank all rest on these results, the claimed significance is not currently realized.

major comments (3)
  1. [§3.2.2 and §4.1.2, Eq. (4), Appendix B] The object called elasticity in Theorem 2 is not the quantity defined in Eq. (2). Eq. (2) defines E_{r,p}=∂v_r/∂v_p, while Theorem 2 states E_{r,p}=∂v_p/∂v_r and gives the closed form (vbar_r/vbar_p)^{-|t|}. The Appendix B calculation is a ratio of partial derivatives of f with respect to v_r and v_p, which is neither of these derivatives. Moreover, because vbar_g=v_g/S, the chain rule introduces factors (1−vbar_g)/S and cross terms between groups that are absent from Eq. (4). The closed-form elasticity and the associated rich/poor threshold therefore do not follow from the stated definitions, so the central claim that the accuracy–fairness trade-off is governed by this elasticity has no valid quantitative derivation.
  2. [§5.1, Theorem 3, Appendix C] The claimed equivalence between the linear objective in Eq. (6) and the product objective in Eq. (7) is not proved. The proof observes that l(v;r) is continuous and that the feasible region is convex, and it then asserts the existence of a λ making the two objectives coincide. Continuity and convexity do not imply that a linear combination and a product have the same argmax. The claim is in fact false for |G|=2 and t=2: Eq. (7) reduces to (vbar_p^{-1}+vbar_r^{-1})/S, which is maximized when one group utility approaches zero, whereas Eq. (6) with λ≥0 is maximized when the two utilities are equal. The intermediate statement in the proof that L is proportional to ∑_g v_g^{1−|t|} is not algebraically implied by Eq. (7). Consequently, the transfer ratio γ in Eq. (8) is not derived, and the main explanatory mechanism of the paper has no valid quantitative support.
  3. [§4.2, §5.2, §6.2] The evaluation is substantially circular. The EF metric in Eq. (5) is defined as an integral of the same family f(v;t) that is used throughout the paper, and ElasticRank's scoring rule in Eqs. (9)–(10) is constructed from this same family. Reporting EF@K as the primary fairness outcome therefore rewards the proposed method for matching the paper's chosen metric, and the comparison does not independently establish that ElasticRank improves fairness under the metrics used by the baselines. The paper should at least report the baselines' own fairness metrics, or other standard group-fairness measures, alongside the EF-Curve.
minor comments (5)
  1. [§6.2.3] The text says that ElasticRank 'better supports the poor groups (indicated by the highest EF-curve when t > 0)'; the second occurrence of 't > 0' should presumably be 't < 0'.
  2. [Eq. (10)] The arc-length integral given in Eq. (10) is not equal to the polynomial expression shown on the right; the approximation and its domain of validity need a derivation or a citation.
  3. [Algorithm 1] The input list uses 'anchor group radio' where 'ratio' is meant.
  4. [§6.1 and Figure 4] Figure 4's caption and the surrounding text refer to 'FairTax' in one sentence; this should be 'ElasticRank' for consistency with the rest of the paper.
  5. [Appendix C] The notation for the accuracy function alternates between a(w), a(v), and ∑_u w_u; these should be unified.

Circularity Check

2 steps flagged · score 6.0 of 10

Two reductions by construction: the 'elasticity' that is claimed to govern the trade-off is defined as the derivative of the paper's own fairness family f(v;t), and ElasticRank is designed to maximize EF and then evaluated chiefly by EF.

  1. self definitional [Section 3.2.2, Eq. (2); Section 4.1.2, Theorem 2 and Eq. (4)]
    "The parameter t in Eq. (3) represents the tax base, where, upon adding the next item to the ranked list, the rich group r compared to the poor group p will be subjected to a commodity tax with a rate of: E_{r,p} = ∂v_p/∂v_r = (v̄_r/v̄_p)^{-|t|}, which means the elastic value as defined in Eq. (2)."

    The quantity called 'elasticity' is not an independent, measured property of user demand or of an external economic demand curve; it is the ratio of partial derivatives of the paper's own fairness function f(v;t) from Eq. (3), as the Appendix B derivation shows. The central conclusion that the accuracy-fairness trade-off is 'governed by the elasticity of utility between item groups' therefore restates the construction: E_{r,p} is defined from the same f(v;t) family whose derivatives control the trade-off. The economics vocabulary ('commodity tax', 'transfer ratio') is a relabeling of this derivative rather than an externally derived mechanism. Eq. (4) also inverts the direction of Eq. (2), but even setting that aside, the explanation is definitional rather than empirical.

  2. self definitional [Section 1 (Introduction); Section 6.1 (Evaluation); Eqs. (5) and (10)]
    "Furthermore, we introduce ElasticRank, a fair re-ranking algorithm designed to optimize the EF metric. ... For the fairness degree, we use the EF metric defined in Eq. (5)."

    EF is defined by integrating the same fairness family f(v;t) over t (Eq. (5)), and ElasticRank's curved-space distance d(g(i),a) = (1-t)v_g^{-t}(v_a^{1-t} - v_g^{1-t}) (Eq. (10)) is built from the same elasticity derivative. The paper then validates ElasticRank chiefly by EF@K in Table 2, a quantity the algorithm was explicitly designed to maximize, while the baselines optimize other objectives. The empirical claim that ElasticRank 'consistently surpasses' the baselines is therefore substantially fixed by the choice of the algorithm's own metric as the evaluation metric; it is not an independent test of the proposed framework.

full rationale

The paper contains two load-bearing reductions by construction. First, the 'elasticity' E_{r,p} is not measured from user behavior or from an independent economic relation; it is computed as a derivative of the paper's own fairness metric family f(v;t). Consequently, the abstract's claim that the accuracy-fairness trade-off is 'governed by the elasticity of utility between item groups' is a restatement of the fact that the trade-off is governed by the derivative of the chosen fairness function. Second, ElasticRank is introduced as an algorithm 'designed to optimize the EF metric', and EF is the integral of the same f(v;t) family over t. The experiments then use EF@K as the headline fairness measure, so the reported superiority over baselines is partly built into the evaluation metric rather than demonstrated against an independent benchmark. No load-bearing self-citation chain was found: Theorem 1's uniqueness argument relies on the external Kolmogorov-Nagumo result via Lan et al. [18], and although TaxRank and P-MMF are same-group prior work, the paper's central circularity is definitional rather than citation-based. Separately, the proof of Theorem 3 appears mathematically invalid (the asserted argmax equivalence between Eq. (6) and Eq. (7) is not established and is in fact false in simple two-group cases), but that is a correctness concern, not a circularity, so it does not affect this score. Because the central conceptual explanation reduces to a definition and the main empirical claim uses the algorithm's own objective as the evaluation metric, but the EF-Curve framework and the efficiency/NDCG/Loss comparisons retain some independent content, the overall circularity score is 6.

Assumptions & free parameters 5 free parameters · 5 assumptions · 2 invented entities

The central claim rests on a domain analogy, an axiomatically forced fairness family, an unproven equivalence between two optimization objectives, and an inconsistent definition of elasticity. The EF metric and ElasticRank are designed from the same family, so the experimental comparison is substantially circular. These are the main premises a reader would need to accept before the central claim can be trusted.

free parameters (5)
  • tax base t = tuned in [1,2], per-dataset values not reported
    Controls the fairness metric in Eq (3), the elasticity in Eq (4), and the distance in Eq (10); central to the EF score and ElasticRank.
  • anchor group ratio eta% = tuned in [50%,95%]
    Selects the anchor group for distance computation in Algorithm 1; the ablation in Figure 7(b) shows it trades off accuracy and fairness.
  • EF integral bound M = 50
    Used to approximate the infinite integral in Eq (5); changing M changes EF values.
  • interaction threshold L = 5 for Steam and Amazon, 10 for Yelp
    Preprocessing filter that removes users and items with fewer than L interactions; affects data composition and group sizes.
  • infrequent group merging threshold = groups with fewer than 10 items merged
    Grouping choice inherited from prior work [40]; changes the number and composition of item groups and therefore the fairness results.
assumptions (5)
  • domain assumption Fairness intervention can be modeled as a commodity tax imposed on rich item groups and transferred to users as accuracy loss.
    Core analogy introduced in Section 1 and Table 1; not derived from ranking principles.
  • ad hoc to paper A fair re-ranking metric must be continuous, scale-invariant, independent of number of groups, and distributed, and with these axioms the unique family is Eq (3).
    Used in Section 4.1.1 to prove Theorem 1; the distributedness requirement is imposed for scalability and is not inherent to fair ranking.
  • ad hoc to paper The product objective L = f(v;|t|)^|t| * a(w)^(1-|t|) has the same maximizing set as the linear accuracy-fairness objective in Eq (6).
    Assumed in Theorem 3 and Appendix C; the proof cites only continuity and convexity and does not establish the claimed equivalence.
  • ad hoc to paper The ratio of partial derivatives of f with respect to rich and poor group utilities is the relevant elasticity or tax transfer rate.
    Eq (4) in Theorem 2 computes E_{r,p} as (d f / d v_r) / (d f / d v_p), which is a marginal rate of substitution, not the utility elasticity defined in Eq (2).
  • domain assumption Ranking scores s_{u,i} are click probabilities and group utilities are additive sums of scores over ranked lists.
    Eq (1); inherited from the fair-ranking literature and standard in the field.
invented entities (2)
  • EF-Curve and EF metric
    purpose: Evaluation framework intended to measure fair re-ranking algorithm performance across a spectrum of fairness metrics.
    EF is defined via the paper's own f(v;t) family and is used to evaluate the algorithm designed to optimize that family; no external benchmark or user-facing validation is provided.
  • Utility elasticity E_{r,p} = (v_r_bar / v_p_bar)^(-|t|)
    purpose: Claims to quantify the tax transfer rate between rich and poor item groups.
    This quantity is defined from f's derivatives rather than from observed responsiveness of group utilities, and it is used to justify ElasticRank; it is not validated against external economic or behavioral data.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Understanding Accuracy-Fairness Trade-offs in Re-ranking through Elasticity in Economics." pith.science (2026). https://pith.science/paper/4WR7NJZO

@misc{pith2026250414991,
  author       = {Pith},
  title        = {Pith review of: Understanding Accuracy-Fairness Trade-offs in Re-ranking through Elasticity in Economics},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4WR7NJZO}},
  note         = {Machine review of arXiv:2504.14991}
}
read the original abstract

Fairness is an increasingly important factor in re-ranking tasks. Prior work has identified a trade-off between ranking accuracy and item fairness. However, the underlying mechanisms are still not fully understood. An analogy can be drawn between re-ranking and the dynamics of economic transactions. The accuracy-fairness trade-off parallels the coupling of the commodity tax transfer process. Fairness considerations in re-ranking, similar to a commodity tax on suppliers, ultimately translate into a cost passed on to consumers. Analogously, item-side fairness constraints result in a decline in user-side accuracy. In economics, the extent to which commodity tax on the supplier (item fairness) transfers to commodity tax on users (accuracy loss) is formalized using the notion of elasticity. The re-ranking fairness-accuracy trade-off is similarly governed by the elasticity of utility between item groups. This insight underscores the limitations of current fair re-ranking evaluations, which often rely solely on a single fairness metric, hindering comprehensive assessment of fair re-ranking algorithms. Centered around the concept of elasticity, this work presents two significant contributions. We introduce the Elastic Fairness Curve (EF-Curve) as an evaluation framework. This framework enables a comparative analysis of algorithm performance across different elasticity levels, facilitating the selection of the most suitable approach. Furthermore, we propose ElasticRank, a fair re-ranking algorithm that employs elasticity calculations to adjust inter-item distances within a curved space. Experiments on three widely used ranking datasets demonstrate its effectiveness and efficiency.

Figures

Figures reproduced from arXiv: 2504.14991 by the authors.

Figure 1
Figure 1. Parallels between (a) the commodity tax transfer [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. (a) The EF-Curve, where the x-axis is tax base [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. (a) The Elasticity curve when optimizing Jain’s index [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Pareto frontier with different size 𝐾 under Steam. for a given NDCG@K level, ElasticRank achieves superior EF@K values, and for a given EF@K level, it delivers better NDCG@K performance. These results highlight the significant advantage of ElasticRank over the baseline…
Figure 5
Figure 5. Figure 5: EF-Curve for different models with cut-off size [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Group utility 𝒗𝑔 and distance 𝑑 (𝑔, 𝑎) w.r.t. the user 𝑢 arriving numbers, where 𝑔 is one rich group 𝑟 and poor group 𝑝; 𝜂% is set to 90% and 𝑡 to 1.05. (a) Inference speed comparison (b) Ablation study on 𝜂 [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: (a) Inference speed comparison between Elasti [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

51 extracted references · 34 canonical work pages

  1. [1]

    Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, and Luiz Pizzato. 2020. Multi- stakeholder Recommendation: Survey and Research Directions. User Modeling and User-Adapted Interaction 30, 1 (2020), 127–158

    work page 2020

  2. [2]

    Himan Abdollahpouri and Robin Burke. 2019. Multi-stakeholder Recommenda- tion and its Connection to Multi-sided Fairness. arXiv preprint arXiv:1907.13158 (2019)

    arXiv 2019

  3. [3]

    Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher

  4. [4]

    Tolga Bektaş and Adam N Letchford. 2020. Using ℓp-Norms for Fairness in Combinatorial Optimisation. Computers & Operations Research120 (2020), 104975

    work page 2020

  5. [5]

    Patro, Niloy Ganguly, Krishna P Gummadi, and Abhij- nan Chakraborty

    Arpita Biswas, Gourab K. Patro, Niloy Ganguly, Krishna P Gummadi, and Abhij- nan Chakraborty. 2021. Toward Fair Recommendation in Two-sided Platforms. ACM Transactions on the Web (TWEB) 16, 2 (2021), 1–34

    work page 2021

  6. [6]

    Jérémie Bouttier, Philippe Di Francesco, and Emmanuel Guitter. 2003. Geodesic Distance in Planar Graphs. Nuclear physics B 663, 3 (2003), 535–567

    work page 2003

  7. [7]

    Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R

    Flavio P. Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R. Varshney. 2017. Optimized Pre-processing for Dis- crimination Prevention. In Proceedings of the 31st International Conference on Neural Information Processing Systems (Long Beach, California, USA) (NIPS’17). Curran Associates Inc., Red Hook, NY, USA, 3995–4004

    work page 2017

  8. [8]

    Yashar Deldjoo, Vito Walter Anelli, Hamed Zamani, Alejandro Bellogín, and Tom- maso Di Noia. 2019. Recommender Systems Fairness Evaluation via Generalized Cross Entropy. arXiv preprint arXiv:1908.06708 (2019)

    work page Pith review arXiv 2019

Show all 51 references
  1. [9]

    Yashar Deldjoo, Dietmar Jannach, Alejandro Bellogin, Alessandro Difonzo, and Dario Zanzonelli. 2022. A Survey of Research on Fair Recommender Systems. arXiv preprint arXiv:2205.11127 (2022)

    2022 arXiv

  2. [10]

    Virginie Do, Sam Corbett-Davies, Jamal Atif, and Nicolas Usunier. 2021. Two- sided Fairness in Rankings via Lorenz Dominance.Advances in Neural Information Processing Systems 34 (2021), 8596–8608

    2021

  3. [11]

    Virginie Do and Nicolas Usunier. 2022. Optimizing Generalized Gini Indices for Fairness in Rankings. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval . 737–747

    2022

  4. [12]

    Ambedkar Dukkipati. 2010. On Kolmogorov-Nagumo Averages and Nonex- tensive Entropy. In 2010 International Symposium On Information Theory & Its Applications. IEEE, 446–451

    2010

  5. [13]

    Michelle Hanlon and Shane Heitzman. 2010. A Review of Tax Research. Journal of accounting and Economics 50, 2-3 (2010), 127–178

    2010

  6. [14]

    Ruining He and Julian McAuley. 2016. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In proceedings of the 25th international conference on world wide web . 507–517

    2016

  7. [15]

    Thomas Jaenich, Graham McDonald, and Iadh Ounis. 2024. Fairness-Aware Expo- sure Allocation via Adaptive Reranking. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval . 1504–1513

    2024

  8. [16]

    Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive Sequential Recom- mendation. In 2018 IEEE international conference on data mining (ICDM) . IEEE, 197–206

    2018

  9. [17]

    Peter J. Lambert. 1992. The Distribution and Redistribution of Income . Springer

    1992

  10. [18]

    Tian Lan, David Kao, Mung Chiang, and Ashutosh Sabharwal. 2010. An Ax- iomatic Theory of Fairness in Network Resource Allocation. In Proceedings IEEE INFOCOM. 1–9

    2010

  11. [19]

    Yunqi Li, Hanxiong Chen, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2021. User-oriented Fairness in Recommendation. In Proceedings of the Web Conference. 624–632

    2021

  12. [20]

    Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, Juntao Tan, Shuchang Liu, and Yongfeng Zhang. 2022. Fairness in Recommendation: A Survey. arXiv preprint arXiv:2205.13619 (2022)

    2022 arXiv

  13. [21]

    Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, Juntao Tan, Shuchang Liu, and Yongfeng Zhang. 2023. Fairness in Recommendation: Foundations, Methods and Applications. ACM Transactions on Intelligent Systems and Technology 14, 5 (2023), Article No.: 95

    2023

  14. [22]

    Joram Lindenstrauss, Gunnar Olsen, and Yaki Sternfeld. 1978. The Poulsen Simplex. In Annales de l’Institut Fourier, Vol. 28. 91–114

    1978

  15. [23]

    Aldo Lipani. 2016. Fairness in Information Retrieval. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. 1171–1171

    2016

  16. [24]

    Xiangyu Liu, Chuan Yu, Zhilin Zhang, Zhenzhe Zheng, Yu Rong, Hongtao Lv, Da Huo, Yiqing Wang, Dagui Chen, Jian Xu, et al. 2021. Neural Auction: End-to-end Learning of Auction Mechanisms for E-commerce Advertising. In Proceedings of the 27th ACM Conference on Knowledge Discover...

    2021

  17. [25]

    Alexander V Lotov and Kaisa Miettinen. 2008. Visualizing the Pareto frontier. In Multiobjective optimization. Springer, 213–243

    2008

  18. [26]

    Mohammadmehdi Naghiaei, Hossein A Rahmani, and Yashar Deldjoo. 2022. CP- Fair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems. arXiv preprint arXiv:2204.08085 (2022)

    2022 arXiv

  19. [27]

    Birger Nerré. 2001. The Concept of Tax Culture. InProceedings Annual Conference on Taxation and Minutes of the Annual Meeting of the National Tax Association , Vol. 94. JSTOR, 288–295

    2001

  20. [28]

    Patro, Arpita Biswas, Niloy Ganguly, Krishna P

    Gourab K. Patro, Arpita Biswas, Niloy Ganguly, Krishna P. Gummadi, and Ab- hijnan Chakraborty. 2020. FairRec: Two-sided Fairness for Personalized Rec- ommendations in Two-sided Platforms. In Proceedings of the Web Conference . 1194–1204

    2020

  21. [29]

    Frank P. Ramsey. 1927. A Contribution to the Theory of Taxation. The Economic Journal 37, 145 (1927), 47–61

    1927

  22. [30]

    Renato Renner and Stefan Wolf. 2004. Smooth Rényi Entropy and Applications. In Proceedings International Symposium on Information Theory. ISIT 2004 . IEEE, 233

    2004

  23. [31]

    Lionel Robbins. 1997. On the Elasticity of Demand for Income in Terms of Effort. In Economic Science and Political Economy: Selected Articles . Springer, 79–84

    1997

  24. [32]

    Yuta Saito and Thorsten Joachims. 2022. Fair Ranking as Fair Division: Impact- Based Individual Fairness in Ranking. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 1514–1524

    2022

  25. [33]

    Ashudeep Singh and Thorsten Joachims. 2019. Policy Learning for Fairness in Ranking. Advances in Neural Information Processing Systems 32 (2019)

    2019

  26. [34]

    Jiakai Tang, Shiqi Shen, Zhipeng Wang, Zhi Gong, Jingsen Zhang, and Xu Chen

  27. [35]

    Dong Wei, Md Mouinul Islam, Baruch Schieber, and Senjuti Basu Roy. 2022. Rank Aggregation with Proportionate Fairness. In Proceedings of the 2022 international conference on management of data . 262–275

    2022

  28. [36]

    Yao Wu, Jian Cao, Guandong Xu, and Yudong Tan. 2021. TFROM: A Two-sided Fairness-aware Recommendation Model for both Customers and Providers. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . 1013–1022

    2021

  29. [37]

    Zikai Xiong, Niccolò Dalmasso, Alan Mishler, Vamsi K Potluru, Tucker Balch, and Manuela Veloso. 2024. Fairwasp: Fast and optimal fair wasserstein pre- processing. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38. 16120–16128

    2024

  30. [38]

    Chen Xu, Sirui Chen, Jun Xu, Weiran Shen, Xiao Zhang, Gang Wang, and Zhenhua Dong. 2023. P-MMF: Provider Max-min Fairness Re-ranking in Recommender System. In Proceedings of the ACM Web Conference 2023 . 3701–3711

    2023

  31. [39]

    Chen Xu, Zhirui Deng, Clara Rus, Xiaopeng Ye, Yuanna Liu, Jun Xu, Zhicheng Dou, Ji-Rong Wen, and Maarten de Rijke. 2025. FairDiverse: A Com- prehensive Toolkit for Fair and Diverse Information Retrieval Algorithms. arXiv:2502.11883 [cs.IR] https://arxiv.org/abs/2502.11883

    2025 arXiv

  32. [40]

    Chen Xu, Jun Xu, Yiming Ding, Xiao Zhang, and Qi Qi. 2024. FairSync: En- suring Amortized Group Exposure in Distributed Recommendation Retrieval. In Proceedings of the ACM Web Conference 2024 (Singapore, Singapore) (WWW ’24). Association for Computing Machinery, New York, NY, ...

    2024

  33. [41]

    Chen Xu, Xiaopeng Ye, Wenjie Wang, Liang Pang, Jun Xu, and Tat-Seng Chua

  34. [42]

    Hong-Jian Xue, Xinyu Dai, Jianbing Zhang, Shujian Huang, and Jiajun Chen

  35. [43]

    Tao Yang, Zhichao Xu, and Qingyao Ai. 2023. Vertical Allocation-based Fair Exposure Amortizing in Ranking. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (Beijing, China) (SIGIR-AP...

    2023

  36. [44]

    Xun Yang, Yasong Li, Hao Wang, Di Wu, Qing Tan, Jian Xu, and Kun Gai. 2019. Bid Optimization by Multivariable Control in Display Advertising. InProceedings of the 25th ACM international conference on knowledge discovery & data mining . 1966–1974

    2019

  37. [45]

    Xiaopeng Ye, Chen Xu, Jun Xu, Xuyang Xie, Gang Wang, and Zhenhua Dong

  38. [46]

    Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, and Krishna P Gummadi. 2019. Fairness Constraints: A Flexible Approach for Fair Classification. The Journal of Machine Learning Research 20, 1 (2019), 2737–2778

    2019

  39. [50]

    Association for Comput- ing Machinery, New York, NY, USA, 2991–3001

    Guaranteeing Accuracy and Fairness under Fluctuating User Traffic: A Bankruptcy-Inspired Re-ranking Approach (CIKM ’24). Association for Comput- ing Machinery, New York, NY, USA, 2991–3001. https://doi.org/10.1145/3627673. 3679590

    doi

  40. [2017]

    In IJCAI, Vol

    Deep Matrix Factorization Models for Recommender Systems.. In IJCAI, Vol. 17. Melbourne, Australia, 3203–3209

  41. [2019]

    arXiv preprint arXiv:1907.13286 (2019)

    The Unfairness of Popularity Bias in Recommendation. arXiv preprint arXiv:1907.13286 (2019)

    2019 arXiv

  42. [2023]

    In Proceedings of the 17th ACM Conference on Recommender Sys- tems (Singapore, Singapore) (RecSys ’23)

    When Fairness meets Bias: a Debiased Framework for Fairness aware Top-N Recommendation. In Proceedings of the 17th ACM Conference on Recommender Sys- tems (Singapore, Singapore) (RecSys ’23). Association for Computing Machinery, New York, NY, USA, 200–210. https://doi.org/10.1...

  43. [2024]

    In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (Washington DC, USA) (SIGIR ’24)

    A Taxation Perspective for Fair Re-ranking. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (Washington DC, USA) (SIGIR ’24). Association for Computing Machin- ery, New York, NY, USA, 1494–1503. https://doi.org...

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.