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Recommender Ecosystems: A Mechanism Design Perspective on Holistic Modeling and Optimization

AAAI 2024 · Probabilistic and scientific modeling › Molecules and proteins · approach: Cold-start recommendation models · 4 citations (OpenAlex)

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A research-challenges paper arguing that recommender systems should be modeled as recommender ecosystems and optimized with nonstandard economic mechanism design, outlining open problems in preferences, information, strategic behavior, and social choice.

Problem

Myopic, local optimization of recommendations to individual users ignores how the recommender system affects other actors (creators, vendors, advertisers) and degrades long-term user utility and ecosystem health.

Approach

Treat recommender policies as mechanisms in a recommender ecosystem, using economic mechanism design (agent incentives, information asymmetry, strategic behavior, social choice functions) to guide holistic, non-myopic optimization, and outline the nonstandard MD research challenges this raises.

Key results

• Illustrative stylized example (two creators, eight users) shows that a myopic matching leads to creator P2 leaving and blue users being worse off in the long run, while a non-myopic ecosystem-aware matching improves long-run user welfare at a small cost to one user and creator P1.

Surprising findings

• Maximizing long-run user welfare can require the RS to give a nearly indifferent user a worse match, sacrificing a small amount of utility to keep creators on the platform.

Limitations

• Stylized model with many simplifying assumptions (stationary preferences, full information, no outside options) that the paper acknowledges are unrealistic
• No empirical evaluation on real recommender data; the agenda is conceptual

Experimental setup

Builds on: classical mechanism design two-sided matching markets Mladenov et al. 2020 constrained matching approach

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