Creator Incentives in Recommender Systems: A Cooperative Game-Theoretic Approach for Stable and Fair Collaboration in Multi-Agent Bandits
A new game-theory framework shows when content creators should pool data on recommendation platforms — and how to split the resulting gains fairly.
The Thesis
Recommendation algorithms on platforms like YouTube or Spotify don't just serve users — they pit creators against each other for exposure. This paper asks: what if creators collaborated instead, pooling their interaction data to train a shared recommendation model, and then dividing the benefits? The authors model this using cooperative game theory, specifically a 'transferable utility' game where each coalition of creators is assigned a value based on how much faster they learn together versus alone. For creators who are similar to each other (homogeneous agents), the math works out neatly: there always exists a stable, fair division of gains that no subgroup would want to defect from. For dissimilar creators, stability is still achievable, but the fairest split — known as the Shapley value, a standard fairness concept from game theory — may no longer be achievable. The practical catch is that this framework is largely theoretical; real deployment would require platforms to expose collaboration mechanisms that most do not today.
Catalyst
Multi-stakeholder fairness in AI systems has become a regulatory and commercial pressure point, with the EU AI Act and platform antitrust scrutiny forcing platforms to justify algorithmic treatment of creators. Separately, the multi-armed bandit literature — the field that models sequential recommendation as a learn-while-you-serve problem — has matured enough that regret bounds are now tight enough to support game-theoretic comparisons between agents. These two threads converging made this synthesis timely.
What's New
Prior work on fairness in recommendation systems focused on the user side — ensuring diverse or unbiased content delivery — or treated creators as passive recipients of algorithmic exposure. Multi-agent bandit research existed but typically assumed agents were competitors or acted independently, not as potential cooperators with formal incentive structures. This paper is the first to apply cooperative game theory (specifically, the core and Shapley value from coalitional game theory) to the creator side of a bandit-based recommender, deriving conditions under which stable collaboration is mathematically guaranteed.
The Counter
The theoretical guarantees here are elegant but rest on assumptions that don't survive contact with real platforms. The 'homogeneous agents' case — where everything works out neatly — requires creators to have identical action sets and algorithmic conditions that no major platform actually enforces. The heterogeneous case, which is the realistic one, loses the strongest guarantees: the Shapley value may not be in the core, and the proposed payout rule only satisfies three of four fairness axioms, which the authors acknowledge. Empirical validation is limited to MovieLens-100k, a decades-old academic dataset with 100,000 ratings that bears little resemblance to TikTok's billion-interaction-per-day environment. Perhaps most critically, the entire framework assumes creators can and would share data — a legal, competitive, and privacy minefield that the paper doesn't seriously engage with. Platforms have little incentive to implement this, since algorithmic opacity is itself a competitive moat. This is a solid theoretical contribution to an academic literature, but the path from here to a deployed system is long and unclear.
Longs
- META — largest creator ecosystem with direct incentive to reduce creator churn via fairer algorithmic treatment
- SPOT (Spotify) — podcast and music creator relations increasingly driven by algorithmic fairness concerns
- GOOGL — YouTube creator monetization depends on recommendation stability
- MTCH (Match Group) — recommendation-heavy consumer platforms facing creator/profile exposure fairness pressure
Shorts
- Creator monetization intermediaries (e.g., Jellysmack, Spotter) — their value prop depends partly on opacity in platform algorithmic treatment; formal fairness frameworks reduce information asymmetry
- Platform recommendation teams using opaque engagement-maximization objectives — regulatory pressure + frameworks like this could force disclosure of creator-side allocation logic
Enablers (Picks & Shovels)
- MovieLens dataset (GroupLens Research, open) — used directly in the paper's experiments
- OpenAI Gymnasium / Bandit simulation libraries — standard testbeds for multi-armed bandit research
- Shapley value computation libraries such as SHAP (open source) — practical tools for approximating the fairness metric discussed
- arXiv cooperative game theory literature — foundational theoretical scaffolding the paper builds on
Private Watchlist
- Recsys.ai (private) — recommendation infrastructure startups building multi-stakeholder ranking tools
- Cohere (private) — enterprise AI with content personalization use cases
- Inflection AI (private) — conversational recommendation and creator-facing AI products
Resources
The Paper
User interactions in online recommendation platforms create interdependencies among content creators: feedback on one creator's content influences the system's learning and, in turn, the exposure of other creators' contents. To analyze incentives in such settings, we model collaboration as a multi-agent stochastic linear bandit problem with a transferable utility (TU) cooperative game formulation, where a coalition's value equals the negative sum of its members' cumulative regrets. We show that, for identical (homogenous) agents with fixed action sets, the induced TU game is convex under mild algorithmic conditions, implying a non-empty core that contains the Shapley value and ensures both stability and fairness. For heterogeneous agents, the game still admits a non-empty core, though convexity and Shapley value core-membership are no longer guaranteed. To address this, we propose a simple regret-based payout rule that satisfies three out of the four Shapley axioms and also lies in the core. Experiments on MovieLens-100k dataset illustrate when the empirical payout aligns with -- and diverges from -- the Shapley fairness across different settings and algorithms.