  Sponsored listing is one of the major revenue sources for many prominent
online marketplaces, such as Amazon, Walmart, and Alibaba. When consumers visit
a marketplace's webpage for a specific item, in addition to that item, the
marketplace might also display a ranked listing of sponsored items from various
third-party sellers. These sellers are charged an advertisement fee if a user
purchases any of the sponsored items from this listing. Determining how to rank
these sponsored items for each incoming visit is a crucial challenge for online
marketplaces, a problem known as sponsored listings ranking (SLR). The major
difficulty of SLR lies in balancing the trade-off between maximizing the
overall revenue and recommending high-quality and relevant ranked listings.
While a more relevant ranking may result in more purchases and consumer
engagement, the marketplace also needs to take account of the potential revenue
when making ranking decisions. Due to the latency requirement and historical
reasons, many online marketplaces use score-based ranking algorithms for SLR
optimization. Alternatively, recent research also discusses obtaining the
ranking by solving linear programming (LP). In this paper, we collaborate with
a leading online global marketplace and conduct a series of field experiments
to compare the performance of the score-based ranking algorithms and the
LP-based algorithms. The field experiment lasted for $19$ days, which included
$329.3$ million visits in total. We observed that the LP-based approach
improved all major metrics by $1.80\%$ of revenue, $1.55\%$ of purchase, and
$1.39\%$ of the gross merchandise value (GMV), compared to an extremely-tuned
score-based algorithm that was previously used in production by the
marketplace.
