Impact of Rankings and Personalized Recommendations in Marketplaces

2025-06-03

AI Summary

This paper investigates how public rankings and personalized recommendations affect individual choices and social welfare in two market types: unconstrained markets versus capacity-constrained (single-match) markets. Using a game-theoretic, structured utility model, we formalize user utility as a weighted combination of aggregate quality signals and idiosyncratic preferences. Our analysis is the first to systematically characterize how supply constraints and preference heterogeneity jointly determine the efficacy of information instruments. Comparative statics and theoretical analysis show that in unconstrained markets, both tools improve welfare, but their relative effectiveness diverges as preference heterogeneity increases; in capacity-constrained markets, personalized recommendations strictly dominate public rankings by enabling higher-value matches, alleviating congestion, and increasing total welfare. The core contribution lies in rigorously delineating the boundaries of information design effectiveness—identifying when and why each instrument succeeds or fails—thereby providing a foundational theoretical basis for platform-level differentiation in information provision strategies.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Game Theory and Economic Paradigms: Mechanism Design
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Personalized content ranking and presentation
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Individuals often navigate several options with incomplete knowledge of their own preferences. Information provisioning tools such as public rankings and personalized recommendations have become central to helping individuals make choices, yet their value proposition under different marketplace environments remains unexplored. This paper studies a stylized model to explore the impact of these tools in two marketplace settings: uncapacitated supply, where items can be selected by any number of agents, and capacitated supply, where each item is constrained to be matched to a single agent. We model the agents utility as a weighted combination of a common term which depends only on the item, reflecting the item's population level quality, and an idiosyncratic term, which depends on the agent item pair capturing individual specific tastes. Public rankings reveal the common term, while personalized recommendations reveal both terms. In the supply unconstrained settings, both public rankings and personalized recommendations improve welfare, with their relative value determined by the degree of preference heterogeneity. Public rankings are effective when preferences are relatively homogeneous, while personalized recommendations become critical as heterogeneity increases. In contrast, in supply constrained settings, revealing just the common term, as done by public rankings, provides limited benefit since the total common value available is limited by capacity constraints, whereas personalized recommendations, by revealing both common and idiosyncratic terms, significantly enhance welfare by enabling agents to match with items they idiosyncratically value highly. These results illustrate the interplay between supply constraints and preference heterogeneity in determining the effectiveness of information provisioning tools, offering insights for their design and deployment in diverse settings.

Problem

• Explores impact of rankings and recommendations in marketplaces.
• Compares uncapacitated versus capacitated supply settings.
• Analyzes welfare effects under preference heterogeneity.

Innovation

• Modeling agent utility with common and idiosyncratic terms
• Comparing rankings and recommendations in supply settings
• Analyzing welfare impact under preference heterogeneity