Online Dynamic Pricing of Complementary Products

2025-11-27

AI Summary

This paper addresses the suboptimal revenue performance in dynamic pricing of complementary products arising from neglecting demand interdependencies. To tackle this, we propose a synergistic pricing framework that jointly models demand structure and enables online learning. Methodologically, we develop a heteroscedastic Gaussian process model incorporating both positive and negative demand interactions, integrate it with a multi-armed bandit for sequential decision-making, and employ integer programming to automatically discover complementarity relationships. Unlike conventional single-product optimization paradigms, our approach enables end-to-end learning of joint pricing policies. In simulation experiments, our method significantly outperforms baseline algorithms that ignore demand interactions, demonstrating the critical role of explicit complementarity modeling in enhancing dynamic pricing efficacy. The framework offers a novel, interpretable, and scalable paradigm for data-driven retail pricing.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Online Learning & Bandits
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Dynamic pricing for complementary products to maximize revenue
• Addressing interdependencies in consumer demand across related goods
• Optimizing coordinated pricing strategies using online learning algorithms

Innovation

• Online learning algorithm for complementary product pricing
• Integer programming identifies product demand interactions
• Heteroscedastic Gaussian processes optimize multi-armed bandit strategies