中文摘要
在去中心化电子商务平台上，搜索算法在匹配买卖双方方面发挥着关键作用。典型的搜索算法会不断精炼并改进其数据目录以提高搜索精度，但更高精度搜索的影响却鲜为人知。我们通过一家全球领先电子商务平台于 2019 年开展的一项准实验来评估此类影响，在该实验中，搜索算法将部分产品类目细分为更精细的子组，从而将消费者查询分配给更相关的商品列表。我们的数据覆盖数百万消费者在六个月内跨多个搜索会话和产品类目的搜索与购买行为，使我们能够考察跨时间和跨类目的权衡。我们发现，更高精度的搜索算法会大幅且即时地提升消费者的点击率和购买率，但其代价是在更长的时间跨度内消费者参与度和计划外购买大幅下降。平均而言，在搜索精度提高后的一周内，过去花费更多时间搜索的消费者的搜索次数减少了 5.5%，在平台上的停留时间减少了 4.1%，在相关类目上的支出减少了 2.2%。我们对这些后果背后机制的考察表明，需要更为审慎的搜索算法设计，不仅要考虑基于搜索精度的即时转化，还要考虑更长跨度内的消费者参与度和卖家的策略性反应。
Abstract
On decentralized e-commerce platforms, search algorithms play a critical role in matching buyers and sellers. A typical search algorithm routinely refines and improves its catalog of data to increase search precision, but the effects of a more precise search are little known. We evaluate such effects via a 2019 quasiexperiment on a world-leading e-commerce platform in which the search algorithm refined some product categories into finer subgroups to allocate consumer queries to more relevant product listings. Our data cover millions of consumers’ search and purchase behaviors over six months across multiple search sessions and product categories, enabling us to investigate trade-offs over time and across categories. We find that a more precise search algorithm improves consumers’ click-through and purchase rates drastically and instantaneously, but it comes at the cost of a significant decrease in consumer engagement and unplanned purchases over a longer time horizon. On average, consumers who used to spend more time searching now conduct 5.5% fewer searches, spend 4.1% less time on the platform, and decrease their spending on related categories by 2.2% in the week after the search precision increases. Our examination of the mechanisms behind these consequences calls for more careful search algorithm designs that account for not only instant conversion based on search precision but also consumer engagement and sellers’ strategic responses in the longer horizon.