在竞争环境下优化分类模型，提升市场占有率。
A Market for Accuracy: Classification under Competition
- 基于市场竞争设计新分类目标，兼顾准确率与竞争策略。
- 实验显示模型能快速收敛至稳定市场均衡。
- 适合关注市场动态的算法服务提供商使用。
机器学习模型在消费者市场中帮助服务提供商争夺份额。然而，传统学习方法未考虑竞争者存在。本文从学习目标出发分析竞争市场，指出准确率不能是唯一考量。提出一种面向竞争的分类方法，使学习者能在对手存在时最大化市场占有率。结果表明该方法既提升提供商收益，也惠及消费者。市场进入时机和模型更新时间对结果至关重要。在简单分布到含噪数据集的多种场景中验证了方法有效性，市场整体快速趋于稳定均衡。
原文摘要 · Abstract (English)
Machine learning models play a key role for service providers looking to gain market share in consumer markets. However, traditional learning approaches do not take into account the existence of additional providers, who compete with each other for consumers. Our work aims to study learning in this market setting, as it affects providers, consumers, and the market itself. We begin by analyzing such markets through the lens of the learning objective, and show that accuracy cannot be the only consideration. We then propose a method for classification under competition, so that a learner can maximize market share in the presence of competitors. We show that our approach benefits the providers as well as the consumers, and find that the timing of market entry and model updates can be crucial. We display the effectiveness of our approach across a range of domains, from simple distributions to noisy datasets, and show that the market as a whole remains stable by converging quickly to an equilibrium.
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