Assortment and Procurement Design in Dual-Mode Content Platforms

2026-09-02

AI Summary

研究数字内容平台如何通过优化内容组合和采购决策来最大化利润，针对用户异质性提出了一种可扩展的近似框架。

Abstract

We study assortment and procurement design for a digital content platform offering both ad-supported and subscription access. Users are heterogeneous in content preferences and ad tolerance and self-select between the two modes or an outside option. For a fixed common subscription price and ad load, the platform chooses assortment distributions specific to each user type and access mode, together with content-family-level buy-versus-rent decisions to maximize profit. Rental costs scale with realized consumption, whereas buying provides a reusable pool of titles whose cost depends on the largest induced requirement across user types and modes. We show that the resulting problem is NP-hard. We then develop a scalable approximation framework based on a candidate buy set, a relaxation of the procurement coupling, and a decomposition into linear programs with a single equality constraint. These subproblems are solved by dual bisection with cardinality-constrained assortment optimization, followed by restricted-master postprocessing to recover primal feasibility. The method yields computable optimality-gap bounds, an interpretable threshold-based procurement heuristic, and asymptotic optimality under proportional market scaling as market size and grid resolution increase. Numerical experiments show strong performance at moderate market scales and grid sizes.

Problem

• assortment
• procurement
• digital content platform
• ad-supported
• subscription

Innovation

• assortment and procurement design
• digital content platform
• NP-hard problem
• approximation framework
• dual bisection