Algorithms battling in a digital marketplace

Algorithmic Showdown: Can Recommender Systems Fight Collusion?

Rhea Morgan in Tech & Innovation December 2025 • 4 min read.

"Exploring how platform algorithms can either fuel or prevent anti-competitive pricing in online markets."

In the digital age, algorithms increasingly shape how we discover and purchase goods online. While AI-powered pricing algorithms have drawn scrutiny for potentially enabling collusion and anti-competitive practices, the role of recommender systems in this landscape is often overlooked. These systems, designed to guide consumer choices, also wield considerable influence over pricing dynamics and market equilibrium.

Recent research has explored algorithmic collusion, where AI pricing tools coordinate to set artificially high prices. However, a new study sheds light on how recommender systems—the algorithms that suggest products to consumers—impact these pricing strategies. By influencing product visibility and consumer choices, recommender systems can inadvertently affect the competitive balance.

The study introduces a framework to analyze how different types of recommendation algorithms impact pricing dynamics and market outcomes. It examines structural search models to characterize consumer decision-making, incorporating both observable and unobservable factors that influence utility and search costs. The research then uses these models to formulate personalized recommendation algorithms designed to maximize either platform revenue or consumer utility, and studies the affects.

Revenue vs. Utility: The Heart of the Recommendation Engine

Algorithms battling in a digital marketplace

The researchers found that revenue-maximizing recommender systems tend to intensify algorithmic collusion. By prioritizing products that generate higher revenue for the platform, these systems inadvertently encourage sellers to maintain elevated prices. This creates a feedback loop where high prices are sustained by the recommender system's choices.

In contrast, utility-maximizing recommender systems promote more competitive pricing behavior. By focusing on consumer satisfaction and suggesting products that offer the best value, these systems disrupt collusive patterns and encourage sellers to lower prices to attract buyers.

Revenue-Maximization: Aims to boost platform revenue, potentially leading to higher prices for consumers.
Utility-Maximization: Focuses on enhancing consumer satisfaction, which can drive more competitive pricing.
The "More is Less" Effect: Expanding recommendations under a utility-maximizing approach doesn't always benefit consumers, highlighting the complexity of algorithmic influence.

Interestingly, the study also challenges conventional wisdom about the benefits of larger recommendation sets. Under a utility-maximizing regime, increasing the size of the recommendation set doesn't consistently improve consumer utility. This "more is less" effect occurs because a larger set reduces the recommender system's ability to effectively influence pricing strategies. When consumers have too many options, the recommender system's subtle guidance becomes diluted, allowing high-priced products to maintain demand based on consumer preferences rather than price considerations.

Implications for Regulators, Platforms, and Sellers

These findings provide valuable insights for regulators, platform designers, and market participants. Regulators seeking to curb algorithmic collusion should consider the influence of recommender systems, not just pricing algorithms. Platforms need to carefully design recommendation policies that balance revenue goals with consumer welfare, and sellers should coordinate product design and pricing strategies to adapt to the platform's recommendation criteria. The interplay between pricing algorithms and recommender systems presents a complex challenge, requiring a holistic approach to ensure fair and competitive online markets.

About this Article -

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This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2309.14548,

Title: Algorithmic Collusion Or Competition: The Role Of Platforms' Recommender Systems

Subject: cs.ai cs.ir econ.gn q-fin.ec

Authors: Xingchen Xu, Stephanie Lee, Yong Tan

Published: 25-09-2023

Everything You Need To Know

How do recommender systems on online platforms affect the prices we see?

Recommender systems play a significant role in influencing pricing dynamics. While AI pricing tools have been scrutinized for potential algorithmic collusion, recommender systems, which guide consumer choices, can either intensify or mitigate such practices. Revenue-maximizing recommender systems may inadvertently encourage sellers to maintain higher prices, while utility-maximizing recommender systems can promote more competitive pricing by focusing on consumer satisfaction. This interplay affects the overall market equilibrium.

What's the difference between revenue-maximizing and utility-maximizing recommender systems, and how do they impact algorithmic collusion?

Revenue-maximizing recommender systems prioritize products that generate higher revenue for the platform, often leading to sustained high prices due to a feedback loop where the system's choices reinforce elevated prices. In contrast, utility-maximizing recommender systems focus on enhancing consumer satisfaction by suggesting products that offer the best value, which can disrupt collusive patterns and encourage sellers to lower prices to attract buyers. The choice between these two approaches significantly impacts the extent of algorithmic collusion in online markets.

Is it always better to have more choices in recommendations from a utility-maximizing system?

Not necessarily. The 'more is less' effect demonstrates that increasing the size of the recommendation set under a utility-maximizing approach doesn't consistently improve consumer utility. A larger set can reduce the recommender system's ability to effectively influence pricing strategies. When consumers have too many options, the subtle guidance of the system becomes diluted, allowing high-priced products to maintain demand based on consumer preferences rather than price considerations. Therefore, the effectiveness of a utility-maximizing system depends on the balance between choice and targeted guidance.

How can regulators use this information about recommender systems to prevent unfair pricing?

Regulators need to broaden their focus beyond just pricing algorithms and consider the influence of recommender systems. By understanding how these systems impact pricing strategies, regulators can develop policies that prevent revenue-maximizing recommender systems from inadvertently promoting algorithmic collusion. They should encourage platforms to adopt utility-maximizing approaches that prioritize consumer welfare. Monitoring and auditing recommendation policies can help ensure a balance between platform revenue goals and fair, competitive online markets.

What strategies should online sellers adopt given the impact of recommender systems on pricing and product visibility?

Sellers should strategically coordinate their product design and pricing strategies to align with the platform's recommendation criteria. Understanding whether a platform uses a revenue-maximizing or utility-maximizing recommender system is crucial. If the platform uses a revenue-maximizing system, sellers might focus on optimizing products for higher revenue generation. Conversely, under a utility-maximizing system, sellers should emphasize competitive pricing and product value to attract buyers. Furthermore, they need to adapt to how the system presents choices to consumers, considering factors like product visibility and the 'more is less' effect to optimize their market positioning.