Paper Number
ICIS2025-2172
Paper Type
Short
Abstract
Online content platforms face a critical design choice between slide mode (single-item display) and feed mode (multi-item display) for their recommendation systems. We develop a stylized model of a two-sided platform to analyze how this choice interacts with the recommendation algorithm's strength and creator incentives. Our analysis reveals a fundamental trade-off: slide mode has a higher per-view monetization ability, while feed mode compensates for weaker algorithms by outsourcing the final content matching to the user. Our key finding is the existence of a performance threshold based on algorithmic capability. Feed mode generates superior revenue when the algorithm is weak; however, as the algorithm strengthens, the matching advantage of the feed mode diminishes, and the slide mode's superior monetization makes it the dominant choice. Practically, our work provides actionable insights, demonstrating that platforms must align their interface design with their algorithmic maturity to optimize performance.
Recommended Citation
zhang, shu; Chen, Hongfan; Zhang, Renyu (Philip); and Zhou, Zhou, "Slide or Feed: Content Recommendation with Creators' Incentives" (2025). ICIS 2025 Proceedings. 25.
https://aisel.aisnet.org/icis2025/hti/hti/25
Slide or Feed: Content Recommendation with Creators' Incentives
Online content platforms face a critical design choice between slide mode (single-item display) and feed mode (multi-item display) for their recommendation systems. We develop a stylized model of a two-sided platform to analyze how this choice interacts with the recommendation algorithm's strength and creator incentives. Our analysis reveals a fundamental trade-off: slide mode has a higher per-view monetization ability, while feed mode compensates for weaker algorithms by outsourcing the final content matching to the user. Our key finding is the existence of a performance threshold based on algorithmic capability. Feed mode generates superior revenue when the algorithm is weak; however, as the algorithm strengthens, the matching advantage of the feed mode diminishes, and the slide mode's superior monetization makes it the dominant choice. Practically, our work provides actionable insights, demonstrating that platforms must align their interface design with their algorithmic maturity to optimize performance.
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