What does this research mean for the field?

Biased product recommendations can be optimal for e-commerce platforms when commission rates are low and product substitutability is high, because recommendation mismatch intensifies seller bidding competition and increases advertising revenue. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To determine why e-commerce platforms deliberately introduce bias into organic product recommendations when sellers compete for sponsored advertising positions.

August 30, 2026International Journal of Production Research

Why platforms bias recommendations: advertising competition among sellers

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Authors

YLYaping Li ZYZhe Yang

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Overview

Theoretical analysis reveals recommendation biasing maximizes platform revenue in competitive seller markets, suggesting that advertising dynamics disincentivize perfectly matched organic results.

Key Points

To determine why e-commerce platforms deliberately introduce bias into organic product recommendations when sellers compete for sponsored advertising positions.
Formulated a game-theoretic model of two sellers with horizontally differentiated products competing on price and bidding for a single sponsored ad slot.
Analyzed platform revenue trade-offs between sales commissions and advertising fees across three organic recommendation regimes: random, unbiased, and biased.
Extended the framework to incorporate consumer search behaviors, specifically mismatch-induced engagement and unplanned impulse purchases.
Biased organic recommendations maximize total platform revenue when product substitutability is high and sales commission rates are low.
Intentionally mismatching organic recommendations reduces commission revenue via lower conversion rates but substantially increases ad revenue by intensifying seller bidding competition.
Winning the sponsored slot under biased recommendations dampens price competition between sellers, enabling higher equilibrium product prices.

Cite This Study

Li et al. (2026) studied this question.

synapsesocial.com/papers/6a93f00a6c1a8fb52e79c1c5 https://doi.org/10.1080/00207543.2026.2720534

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August 30, 2026International Journal of Production Research

Why platforms bias recommendations: advertising competition among sellers

View Full Paper

Ask AI

Bookmark

Authors

YLYaping Li ZYZhe Yang

Discussion

Member takes

Overview

Theoretical analysis reveals recommendation biasing maximizes platform revenue in competitive seller markets, suggesting that advertising dynamics disincentivize perfectly matched organic results.

Key Points

To determine why e-commerce platforms deliberately introduce bias into organic product recommendations when sellers compete for sponsored advertising positions.
Formulated a game-theoretic model of two sellers with horizontally differentiated products competing on price and bidding for a single sponsored ad slot.
Analyzed platform revenue trade-offs between sales commissions and advertising fees across three organic recommendation regimes: random, unbiased, and biased.
Extended the framework to incorporate consumer search behaviors, specifically mismatch-induced engagement and unplanned impulse purchases.
Biased organic recommendations maximize total platform revenue when product substitutability is high and sales commission rates are low.
Intentionally mismatching organic recommendations reduces commission revenue via lower conversion rates but substantially increases ad revenue by intensifying seller bidding competition.
Winning the sponsored slot under biased recommendations dampens price competition between sellers, enabling higher equilibrium product prices.

Cite This Study

Li et al. (2026) studied this question.

synapsesocial.com/papers/6a93f00a6c1a8fb52e79c1c5 https://doi.org/10.1080/00207543.2026.2720534

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