针对互补商品设计动态定价算法，提升联合收益。
Online Dynamic Pricing of Complementary Products
- 通过整数规划识别商品间互补关系，建模需求互动。
- 基于异方差高斯过程的多臂老虎机算法优化定价。
- 在模拟中相比忽略互动的算法提升收入。
传统定价方法多依赖静态模型与规则启发式，正逐渐被由机器学习驱动的动态数据方法取代。尽管技术日益复杂，多数动态定价算法仍独立优化每种商品价格，忽视相关商品间的潜在需求互动。忽略这种相互依赖性会导致无法充分挖掘协同定价策略的潜力。本文提出一种专为互补产品设计的动态定价机制，旨在利用其联合需求结构最大化整体收入。我们设计了一种在线学习算法，同时考虑商品间正负向需求互动。该算法利用交易数据，通过不同商品间的整数规划问题识别有利的互补关系，并基于异方差高斯过程的高效多臂老虎机方法优化定价策略。我们在模拟环境中验证了该方案，结果表明，相较于忽略此类互动的可比学习算法，本方案显著提升了收入。
原文摘要 · Abstract (English)
Traditional pricing paradigms, once dominated by static models and rule-based heuristics, are increasingly being replaced by dynamic, data-driven approaches powered by machine learning algorithms. Despite their growing sophistication, most dynamic pricing algorithms focus on optimizing the price of each product independently, disregarding potential interactions among items. By neglecting these interdependencies in consumer demand across related goods, sellers may fail to capture the full potential of coordinated pricing strategies. In this paper, we address this problem by exploring dynamic pricing mechanisms designed explicitly for complementary products, aiming to exploit their joint demand structure to maximize overall revenue. We present an online learning algorithm considering both positive and negative interactions between products' demands. The algorithm utilizes transaction data to identify advantageous complementary relationships through an integer programming problem between different items, and then optimizes pricing strategies using data-driven and computationally efficient multi-armed bandit solutions based on heteroscedastic Gaussian processes. We validate our solution in a simulated environment, and we demonstrate that our solution improves the revenue w.r.t. a comparable learning algorithm ignoring such interactions.
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