Revenue-Maximizing Rankings for Online Platforms with Quality-Sensitive Consumers
研究搜索引擎或电商平台如何在相关性和预期收入之间权衡排序，提出一种简单易行的排序策略，通过模拟优化找到最优参数，实现长期收入最大化。
When a keyword-based search query is received by a search engine, a classified ads website, or an online retailer site, the platform has exponentially many choices in how to sort the search results. Two extreme rules are (a) to use a ranking based on estimated relevance only, which improves customer experience in the long run because of perceived quality and (b) to use a ranking based only on the expected revenue to be generated immediately, which maximizes short-term revenue. Typically, these two objectives and the corresponding rankings differ. A key question then is what middle ground between them should be chosen. We introduce stochastic models that yield elegant solutions for this situation, and we propose effective solution methods to compute a ranking strategy that optimizes long-term revenues. This strategy has a very simple form and is easy to implement if the necessary data is available. It consists of ordering the output items by decreasing order of a score attributed to each, similarly to value models used in practice by e-commerce platforms. This score results from evaluating a simple function of the estimated relevance, the expected revenue of the link, and a real-valued parameter. We find the latter via simulation-based optimization, and its optimal value is related to the endogeneity of user activity in the platform as a function of the relevance offered to them.