Pith. sign in

REVIEW 3 major objections 5 minor 28 references

Entry Barriers in Content Markets

T0 review · 3 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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

Pith's one-line read This paper proves that rank-order and proportional reward mechanisms create structural entry barriers at Nash equilibrium, and that platforms can improve content quality by charging entry fees and recycling them into rewards.

desk verdict The structural-barrier analysis is credible and worth engaging, but the headline EFRM optimality theorem is unproved and the accompanying algorithm fails on the paper's own example. read the letter →

arxiv 2509.01953 v1 pith:2BLZCAIS submitted 2025-09-02 cs.GT cs.LG

Haiqing Zhu , Lexing Xie , Yun Kuen Cheung This is my paper · ORCID
classification cs.GTcs.LG MSC 91A1091B26
keywords entrybarrierscontentplatformsrank-ordermechanismsproportionalNashequilibriumfeesrewardreallocationqualitymetrics
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 asks whether entry barriers can make online content markets better rather than worse. It shows that under two common reward rules—rank-order rewards, where the best items win fixed prizes, and proportional rewards, where payouts track quality—the equilibrium play of incumbent creators itself deters new entrants, which prevents the market from being flooded by low-effort content. The paper then studies a deliberate barrier: a platform charges each creator a small entry fee and adds the collected fees to the reward pool. Under rank-order rewards, this can weakly improve quality, and the paper identifies exactly how to redistribute the fees to maximize either average quality or the expected quality of the best item. Under proportional rewards, a moderate fee mainly expels the weakest creators and improves quality measures other than the simple sum.

What carries the argument

The load-bearing object is the rank-order reward function h_n(α,t)=Σ_{i=0}^{n-1} α_{i+1} C(n-1,i) t^{n-1-i}(1−t)^i, a creator's expected reward when rivals draw from equilibrium CDF F at quantile t; setting this equal to c(q) pins the symmetric Nash equilibrium and makes the entry-barrier proof quantitative. The Entry Fee Reallocation Mechanism (EFRM) is the feasible set of modified reward vectors, α_i≥max{ξ,α_i} and Σ(α_i−α_i)≤nξ. The two algorithms, Max-Min and Max-Max, are the concrete reallocation rules that push the reward vector toward the quality-optimal mechanisms (equal top-(n−1) rewards and winner-take-all, respectively).

What would settle it

Give two incumbents cost c(q)=q and rank-order rewards (1/2,1/2,0); their symmetric equilibrium is F(q)=1−√(1−2q). Ask whether a third creator with cost c(q)=q/2 can earn positive expected profit by best-responding to F. Theorem 6 predicts zero only for identical cost; a positive profit for a cheaper entrant would show the structural-barrier claim does not survive cost heterogeneity. For the fee result, computing the optimal reallocation under a cost violating c′′≤c′², e.g., c(q)=e^q on [0,1], would test whether Max-Max remains L∞-optimal.

Watch

Extended reading notes

Core claim

The central claim is computational. With rank-order rewards and a common cost c, the symmetric mixed Nash equilibrium has CDF F satisfying h_n(α,F(q))=c(q): expected reward equals cost at every quality in the support. Against that incumbent equilibrium, a new same-type creator earns expected reward no greater than cost, so entry is unprofitable (Theorem 6). When the platform charges each creator a fee ξ and recycles the fees into rewards (the EFRM constraints), Max-Min reallocation maximizes average quality and Max-Max reallocation maximizes expected maximum quality (Theorem 14, assuming c′′≤c′²). Proportional rewards have a unique pure Nash equilibrium whose contributors are exactly the cre

Load-bearing premise

The rank-order structural-barrier theorem assumes all creators share one cost function; the general statement that rank-order mechanisms automatically bar entry is proven only in that symmetric-cost case.

Editorial extensions

If this is right

  • A platform using rank-order rewards cannot be flooded by entrants: incumbents' equilibrium alone blocks unprofitable entry, preventing the average-quality collapse that would occur with unlimited entry.
  • A platform can choose its policy goal: use Max-Min reallocation to maximize mean content quality, or Max-Max reallocation to maximize the expected quality of the best item.
  • There exists a positive-fee EFRM that weakly improves both average and maximum quality, so fee recycling can beat no fee at no cost to either objective.
  • For proportional rewards, an entry fee that is not too large improves quality metrics except total sum; only the weakest creators are driven out, and beyond a threshold the fee harms the market.

Reading between the lines

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

  • Beyond the paper's symmetric case: with heterogeneous creator costs, the structural barrier may not transfer, so the strategic entry-fee results are likely the more load-bearing design tool in real markets.
  • The L1/L∞ split implies a platform cannot maximize both average and best-item quality with one fee schedule; a weighted Lp objective would need an interpolation between Max-Min and Max-Max that the paper does not construct.
  • A flat entry fee ignores differences in who is likely to produce junk; conditioning the fee on observed marginal cost or past quality is a natural extension that could filter more precisely.
  • Because quality signals are noisy in practice, fee recycling could penalize genuinely good creators whose quality is underestimated; pairing EFRM with uncertainty-aware rewards is a testable design direction.
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 models a content market as a contest among n creators who choose quality q_i in [0,1] with convex costs, under either a rank-order (RO) or proportional-share (PM) reward mechanism. It characterizes the symmetric mixed Nash equilibrium for RO (Theorem 2), identifies L_p-optimal RO reward vectors (Propositions 3–5), proves a structural entry barrier for RO when all creators have the same cost (Theorem 6), characterizes the set of contributing creators under PM (Theorems 7 and 10), and then introduces an Entry Fee Reallocation Mechanism (EFRM) that charges an entry fee ξ and redistributes it into the reward pool. The main normative claim is Theorem 14: Max-Min reallocation maximizes the L_1 (average quality) metric and Max-Max reallocation maximizes the L_∞ (expected maximum quality) metric at equilibrium. The paper also includes a simulation for PM with entry fees.

Significance. If the results were fully established, the paper would make a useful first step in the game-theoretic study of entry barriers in content platforms. The equilibrium characterizations in Theorem 2 and Theorem 7/10 are concrete and grounded in standard results (Ghosh–Hummel; Szidarovszky–Okuguchi), and the paper is self-contained with no fitted constants. The structural-barrier idea (Theorem 6) and the c'_k(0) threshold for PM (Theorem 10) are clean and potentially transferable. However, the flagship optimality result, Theorem 14, is currently unsupported: its appendix proof is a placeholder, and the proposed Max-Min algorithm fails on the paper's own 3-creator example. The practical design claim that entry fees can be tuned to target either mean or top-item quality is therefore not yet demonstrated.

major comments (3)
  1. [Appendix L, Theorem 14] The proof is a placeholder: it says both results are 'immediate from Theorem 19 and Theorem 21' and invokes Proposition 26, but Lemmas 18–21 solve the no-fee problem max ∫ c^{-1}(h(α,y))dy over descending α with Σα=1. With an entry fee ξ, the equilibrium indifference condition is h(α,F(q))−ξ = c(q). Writing β_i=α_i−ξ, this becomes h(β,F(q))=c(q), so the objective should be ∫ c^{-1}(h(β,y))dy, with feasible set β descending, β_i≥max{0, α_i−ξ}, Σβ≤1. The appendix never performs this reduction, never checks the EFRM constraints, and Proposition 26 only gives existence of a symmetric equilibrium for a shifted reward scale. The optimality of Max-Min and Max-Max is therefore unproved.
  2. [Section 4.1, Algorithm 1] The stated Max-Min algorithm is internally inconsistent with Theorem 14. For n=3, α=(1/2,1/2,0), ξ=1/2, R=nξ=3/2. After the remedy step, α=(1/2,1/2,1/2) and R=1. The only loop iteration i=1 finds R>0 but the update α_j←α_1 for j≥2 changes nothing, so R is never spent. The output has effective rewards β=(0,0,0), whose NE has L_1=0. Yet the feasible EFRM α=(1,1,1/2) satisfies α_i≥max{ξ,α_i} and Σ(α_i−α_i)=3/2=nξ, and yields β=(1/2,1/2,0) with positive L_1. Thus Theorem 14's first claim is false for the algorithm as written.
  3. [Abstract and Section 3.1, Theorem 6] The RO structural barrier is proved only under the assumption that all creators have the same cost function c. The proof relies on the symmetric equilibrium F_n of Theorem 2; with heterogeneous costs no such symmetric profile exists and the argument does not transfer. The abstract nevertheless states that 'both rank-order and proportional-share reward mechanisms induce such a structural barrier at Nash equilibrium' without qualification, and the surrounding discussion of Top-k reads as a general claim. The paper's own conclusion lists the homogeneous-cost assumption as future work; the abstract and introduction should carry the same qualification.
minor comments (5)
  1. [Section 4.1] The prose preceding Algorithm 1 says Max-Min 'subsidises from the reward of the bottom rank α_n to the top rank α_1', which contradicts the water-filling code that raises lower rewards upward. Please reconcile the description with the pseudocode.
  2. [Lemma 13] Lemma 13 is stated without a proof in the main text or appendix. A one-line proof is available by taking α_i=α_i+ξ, which is feasible and leaves the net effective rewards β=α unchanged; please include it.
  3. [Cross-references] Several references are inconsistent: 'Theorem 12' in Section 4.1 should be Definition 12; 'By Theorem 4' in the EFRM example should be Proposition 4; Proposition 5 is called 'Theorem 5' in the text; Proposition 16 is called 'Theorem 16' in Section 4.2.
  4. [Figure 1] The y-axis label reads 'q p'; it should indicate the L_p norm ∥q∥_p, and the p=∞ curve should be defined explicitly rather than left as a limiting case.
  5. [Typos] There are typos such as 'recommnder' (Conclusion), 'stastisfies' (Lemma 22), and 'P_i∈I αn ≤ 1' in Propositions 3–4, which should be Σα_i ≤ 1.

Circularity Check

1 steps flagged · score 1.0 of 10

No significant circularity: the derivation chain is self-contained; the main weakness is an omitted proof of Theorem 14, which is a correctness gap rather than a self-referential construction.

  1. other [Appendix L (Proof of Theorem 14), relying on Proposition 26]
    "Both results are immediate from Theorem 19 and Theorem 21. Specifically, by Theorem 26, there is no change to the equilibrium structure rather than the flexibility to reassign the entry fees. Theorem 19 pointed out that the max-min mechanism is the optimal way and Theorem 21 pointed out that the max-max mechanism is the optimal way."

    This is not a circularity but an omitted proof flagged for completeness. The appendix does not derive the EFRM-constrained optimization. After a fee ξ, net rewards are β_i = α_i − ξ and Proposition 26 (with c(0)=0) gives the equilibrium condition h(β,F(q)) = c(q) + β_n, so the objective is ∫ c^{-1}(h(β,y) − β_n) dy, not the unshifted objective used in Lemmas 18–21. The feasible set also has lower bounds β_i ≥ α_i − ξ and total Σβ_i ≤ 1, which the lemmas do not cover. Hence Theorem 14's claim is unsupported, but this is a proof gap, not an equivalence-by-construction.

full rationale

The paper's derivation chain is largely self-contained. Theorem 2's NE characterization is derived from the model and uses Ghosh and Hummel (2014) only for existence of a symmetric mixed NE; the functional equation h(α,F(q))=c(q) is then proven in Appendix B. Theorem 6's structural barrier is proven directly from the incumbent NE condition: the entrant's expected reward under the (n+1)-player mechanism is pointwise no larger than the n-player reward, hence ≤ c(q). PM results are built on the external uniqueness theorem of Szidarovszky and Okuguchi (1997), with the barrier condition (1) derived from first-order conditions; no fitted constants or predicted values are used as inputs. The only self-citation (Zhu et al. 2023) supports the interpretation of q as engagement probability and is not load-bearing. The central normative claim, Theorem 14, is not proven: Appendix L is a placeholder asserting the result is 'immediate' from Lemmas 19/21 and Proposition 26, without performing the reduction to net rewards β=α−ξ or checking the EFRM constraints; Proposition 26's note that 'the EFRM always ensures α_n = c(0) = ξ' is also unsupported (c(0)=0 in the model). However, this is an omitted/invalid derivation, not a circular one: the theorem does not define optimality in terms of the asserted conclusion, and no equation reduces to an input by construction. Accordingly, the circularity score is low (1), reflecting only the minor, non-load-bearing self-citation.

Assumptions & free parameters 0 free parameters · 9 assumptions · 0 invented entities

No free parameters are fitted to data; reward vectors α and the entry fee ξ are design variables chosen by the platform, not fitted constants. The axioms listed are the model's substantive assumptions, with two external existence theorems from the cited literature. No entities such as new particles or mediators are introduced; the structural and strategic barriers are economic concepts and EFRM is a mechanism, not an entity.

assumptions (9)
  • domain assumption Creators' cost functions are strictly increasing, twice differentiable, and satisfy c_i(0)=0.
    Section 2, Model. This restricts the action space to quality in [0,1] with zero cost at zero quality; used in all equilibrium characterizations.
  • domain assumption Total reward is normalized to at most 1 and c_i(1) > 1.
    Used in Theorem 6, Theorem 7 proof (Appendix G), and the related-work remarks. Ensures no creator profits from producing perfect content under the given budget.
  • domain assumption RO structural barrier assumes identical cost functions for all creators.
    Section 3.1, Theorem 6. The symmetric mixed NE F_n is only characterized for identical costs; the barrier proof is invalid for heterogeneous costs.
  • domain assumption PM analysis assumes convex cost functions.
    Section 3.2 'throughout this section, we assume the cost functions ci are convex'. Needed for uniqueness of NE (Szidarovszky-Okuguchi) and for threshold participation.
  • domain assumption Content quality is directly observable and rankable.
    Used throughout the RO analysis; stated as an open limitation in the Conclusion. If quality is noisy, the ranking-based reward and the equilibrium change.
  • domain assumption When a new creator joins, the RO reward vector changes componentwise monotonically: α_i^n ≥ α_i^{n+1}, with α_{n+1}^{n+1}=0.
    Theorem 6 condition. This is the mechanism-stability assumption that makes the structural barrier hold; the paper notes it applies to Top-k but not to Top-(n-1).
  • domain assumption For L∞-optimality, cost satisfies c''(q) ≤ c'(q)^2.
    Proposition 4 and Theorem 14. Used in Lemma 22 to ensure monotonicity of (c^{-1})' h^{n-1}; excludes costs with sharply increasing marginal cost.
  • standard math Existence of symmetric mixed NE for RO is taken from Ghosh and Hummel [2014].
    Lemma 17 proof in Appendix B cites this prior result rather than proving existence.
  • standard math Unique pure NE for PM is taken from Szidarovszky and Okuguchi [1997].
    Theorem 7 is cited verbatim as a background result for the PM analysis.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Entry Barriers in Content Markets." pith.science (2026). https://pith.science/paper/2BLZCAIS

@misc{pith2026250901953,
  author       = {Pith},
  title        = {Pith review of: Entry Barriers in Content Markets},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2BLZCAIS}},
  note         = {Machine review of arXiv:2509.01953}
}
read the original abstract

The prevalence of low-quality content on online platforms is often attributed to the absence of meaningful entry requirements. This motivates us to investigate whether implicit or explicit entry barriers, alongside appropriate reward mechanisms, can enhance content quality. We present the first game-theoretic analysis of two distinct types of entry barriers in online content platforms. The first, a structural barrier, emerges from the collective behaviour of incumbent content providers which disadvantages new entrants. We show that both rank-order and proportional-share reward mechanisms induce such a structural barrier at Nash equilibrium. The second, a strategic barrier, involves the platform proactively imposing entry fees to discourage participation from low-quality contributors. We consider a scheme in which the platform redirects some or all of the entry fees into the reward pool. We formally demonstrate that this approach can improve overall content quality. Our findings establish a theoretical foundation for designing reward mechanisms coupled with entry fees to promote higher-quality content and support healthier online ecosystems.

Figures

Figures reproduced from arXiv: 2509.01953 by the authors.

Figure 1
Figure 1. Simulation of the proportional mechanism with [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

28 extracted references · 24 canonical work pages

  1. [1]

    A game-theoretic approach to recommendation systems with strategic content providers

    Omer Ben-Porat and Moshe Tennenholtz. A game-theoretic approach to recommendation systems with strategic content providers. Advances in Neural Information Processing Systems, 31, 2018

    work page 2018

  2. [2]

    Bandit learning in concave n-person games

    Mario Bravo, David Leslie, and Panayotis Mertikopoulos. Bandit learning in concave n-person games. Advances in Neural Information Processing Systems, 31, 2018

    work page 2018

  3. [3]

    Finite-time last-iterate convergence for learning in multi-player games

    Yang Cai, Argyris Oikonomou, and Weiqiang Zheng. Finite-time last-iterate convergence for learning in multi-player games. Advances in Neural Information Processing Systems, 35: 0 33904--33919, 2022

    work page 2022

  4. [4]

    Recherches sur les principes mathematiques de la theorie des richesses

    Antoine Augustin Cournot. Recherches sur les principes mathematiques de la theorie des richesses. Making of the Modern World, 1838

    work page

  5. [5]

    On the convergence of regret minimization dynamics in concave games

    Eyal Even- D ar, Yishay Mansour, and Uri Nadav. On the convergence of regret minimization dynamics in concave games. In Proceedings of the Forty-First Annual ACM Symposium on Theory of Computing, STOC '09, page 523–532, New York, NY, USA, 2009. Association for Computing Machinery. ISBN 9781605585062. doi:10.1145/1536414.1536486. URL https://doi.org/10.114...

    arXiv 2009

  6. [6]

    A game-theoretic analysis of rank-order mechanisms for user-generated content

    Arpita Ghosh and Patrick Hummel. A game-theoretic analysis of rank-order mechanisms for user-generated content. Journal of Economic Theory, 154: 0 349--374, 2014. ISSN 0022-0531. doi:https://doi.org/10.1016/j.jet.2014.09.009. URL https://www.sciencedirect.com/science/article/pii/S0022053114001306

    doi:10.1016/j.jet.2014.09.009 2014

  7. [7]

    Incentivizing high-quality user-generated content

    Arpita Ghosh and Preston McAfee. Incentivizing high-quality user-generated content. In Proceedings of the 20th international conference on World wide web, pages 137--146, 2011

    work page 2011

  8. [8]

    Equilibrium of data markets with externality

    Safwan Hossain and Yiling Chen. Equilibrium of data markets with externality. In Proceedings of the 40th International Conference on Machine Learning, Proceedings of Machine Learning Research. PMLR, 2024

    work page 2024

Show all 28 references
  1. [9]

    Jordan, Niki Kilbertus, and Sarah Dean

    Jiri Hron, Karl Krauth, Michael I. Jordan, Niki Kilbertus, and Sarah Dean. Modeling content creator incentives on algorithm-curated platforms. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023 . OpenReview.net, 2023....

    2023

  2. [10]

    Supply-side equilibria in recommender systems

    Meena Jagadeesan, Nikhil Garg, and Jacob Steinhardt. Supply-side equilibria in recommender systems. Advances in Neural Information Processing Systems, 36: 0 14597--14608, 2023

    2023

  3. [11]

    Rate control for communication networks: shadow prices, proportional fairness and stability

    Frank P Kelly, Aman K Maulloo, and David Kim Hong Tan. Rate control for communication networks: shadow prices, proportional fairness and stability. Journal of the Operational Research society, 49 0 (3): 0 237--252, 1998

    1998

  4. [12]

    McAfee, Hugo M

    Preston R. McAfee, Hugo M. Mialon, and Michael A. Williams. What is a barrier to entry? American Economic Review, 94 0 (2): 0 461–465, May 2004. doi:10.1257/0002828041302235. URL https://www.aeaweb.org/articles?id=10.1257/0002828041302235

    2004 doi

  5. [13]

    Quasi-proportional mechanisms: Prior-free revenue maximization

    Vahab Mirrokni, S Muthukrishnan, and Uri Nadav. Quasi-proportional mechanisms: Prior-free revenue maximization. In Latin American Symposium on Theoretical Informatics, pages 565--576. Springer, 2010

    2010

  6. [14]

    Digital content creation: An analysis of the impact of recommendation systems

    Kun Qian and Sanjay Jain. Digital content creation: An analysis of the impact of recommendation systems. Management Science, 2024

    2024

  7. [15]

    J. B. Rosen. Existence and uniqueness of equilibrium points for concave n-person games. Econometrica, 33 0 (3): 0 520--534, 1965. ISSN 00129682, 14680262. URL http://www.jstor.org/stable/1911749

    1965

  8. [16]

    Experimental study of inequality and unpredictability in an artificial cultural market

    Matthew J Salganik, Peter Sheridan Dodds, and Duncan J Watts. Experimental study of inequality and unpredictability in an artificial cultural market. science, 311 0 (5762): 0 854--856, 2006

    2006

  9. [17]

    A proportional share resource allocation algorithm for real-time, time-shared systems

    Ion Stoica, Hussein Abdel-Wahab, Kevin Jeffay, Sanjoy K Baruah, Johannes E Gehrke, and C Greg Plaxton. A proportional share resource allocation algorithm for real-time, time-shared systems. In 17th IEEE Real-Time Systems Symposium, pages 288--299. IEEE, 1996

    1996

  10. [18]

    On the existence and uniqueness of pure nash equilibrium in rent-seeking games

    Ferenc Szidarovszky and Koji Okuguchi. On the existence and uniqueness of pure nash equilibrium in rent-seeking games. Games and Economic Behavior, 18 0 (1): 0 135--140, 1997. ISSN 0899-8256. doi:https://doi.org/10.1006/game.1997.0517. URL https://www.sciencedirect.com/science...

    1997

  11. [19]

    Creator rewards program, 2024

    TikTok. Creator rewards program, 2024. Available at: https://www.tiktok.com/creator-academy/en/article/creator-rewards-program, accessed in Aug, 2024

    2024

  12. [20]

    The welfare costs of tariffs, monopolies, and theft

    Gordon Tullock. The welfare costs of tariffs, monopolies, and theft. Western Economic Journal, 5 0 (3): 0 224--232, 1967

    1967

  13. [21]

    Jeremy K. West. Barriers to entry. Technical Report Working Paper No. 58, OECD Roundtables on Competition Policy, 2005. URL https://ssrn.com/abstract=893162

    2005

  14. [22]

    Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang, and Haifeng Xu. How bad is top- k recommendation under competing content creators? In Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett, editors, Proceedings of the 40th ...

    2023

  15. [23]

    Human vs

    Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang, and Haifeng Xu. Human vs. generative ai in content creation competition: symbiosis or conflict? arXiv preprint arXiv:2402.15467, 2024 a

    2024 arXiv

  16. [24]

    Rethinking incentives in recommender systems: are monotone rewards always beneficial? Advances in Neural Information Processing Systems, 36, 2024 b

    Fan Yao, Chuanhao Li, Karthik Abinav Sankararaman, Yiming Liao, Yan Zhu, Qifan Wang, Hongning Wang, and Haifeng Xu. Rethinking incentives in recommender systems: are monotone rewards always beneficial? Advances in Neural Information Processing Systems, 36, 2024 b

    2024

  17. [25]

    Unveiling user satisfaction and creator productivity trade-offs in recommendation platforms

    Fan Yao, Yiming Liao, Jingzhou Liu, Shaoliang Nie, Qifan Wang, Haifeng Xu, and Hongning Wang. Unveiling user satisfaction and creator productivity trade-offs in recommendation platforms. arXiv preprint arXiv:2410.23683, 2024 c

    2024 arXiv

  18. [26]

    User welfare optimization in recommender systems with competing content creators

    Fan Yao, Yiming Liao, Mingzhe Wu, Chuanhao Li, Yan Zhu, James Yang, Jingzhou Liu, Qifan Wang, Haifeng Xu, and Hongning Wang. User welfare optimization in recommender systems with competing content creators. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discover...

    2024

  19. [27]

    Youtube partner program, 2024

    YouTube. Youtube partner program, 2024. Available at: https://support.google.com/youtube/answer/72851?hl=en, accessed in Aug, 2024

    2024

  20. [28]

    Stability and efficiency of personalised cultural markets

    Haiqing Zhu, Yun Kuen Cheung, and Lexing Xie. Stability and efficiency of personalised cultural markets. In Proceedings of the ACM Web Conference 2023, pages 3447--3455, 2023

    2023

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.