中文摘要
许多双边市场依赖匹配推荐来帮助顾客以合适的价格找到合适的服务提供者。本文提出一种可操作的方法,平台可据此优化其匹配推荐策略,以最大化平台所创造的总价值,同时将交易价格的内生性纳入考量——交易价格由提供者依据供求关系设定,且可能取决于平台的匹配推荐策略。尽管价格内生性带来了复杂性,最优匹配推荐策略却具有简单的结构,且可以被高效计算。具体而言,最优策略总是推荐转化率最高的提供者。此外,最优策略可以仅用向每个顾客细分群体推荐各提供者的频率来简洁地编码,而无需编码哪些提供者子集应被一并推荐。另一方面,如果平台在优化匹配推荐时不考虑价格内生性,那么所得策略将更为复杂,且市场很可能陷入严格次优的结果,即便平台在价格重新均衡后不断重新优化其匹配推荐也是如此。
Abstract
Many two-sided marketplaces rely on match recommendations to help customers find suitable service providers at suitable prices. This paper develops a tractable methodology that a platform can use to optimize its match recommendation policy to maximize the total value generated by the platform while accounting for the endogeneity of transaction prices, which are set by the providers based on supply and demand and can depend on the platform’s match recommendation policy. Despite the complications of price endogeneity, an optimal match recommendation policy has a simple structure and can be computed efficiently. In particular, an optimal policy always recommends the providers who deliver the highest conversion rates. Moreover, an optimal policy can be encoded simply in terms of the frequency of recommending each provider to each customer segment, without the need to encode which subsets of providers are to be recommended together. On the other hand, if the platform were to optimize its match recommendations without accounting for price endogeneity, then the resultant policy would be more complex, and the market is likely to get stuck at a strictly suboptimal outcome, even if the platform were to continually reoptimize its match recommendations after prices re-equilibrate.