  Two-sided marketplaces such as eBay, Etsy and Taobao have two distinct groups
of customers: buyers who use the platform to seek the most relevant and
interesting item to purchase and sellers who view the same platform as a tool
to reach out to their audience and grow their business. Additionally, platforms
have their own objectives ranging from growing both buyer and seller user bases
to revenue maximization. It is not difficult to see that it would be
challenging to obtain a globally favorable outcome for all parties. Taking the
search experience as an example, any interventions are likely to impact either
buyers or sellers unfairly to course correct for a greater perceived need. In
this paper, we address how a company-aligned search experience can be provided
with competing business metrics that E-commerce companies typically tackle. As
far as we know, this is a pioneering work to consider multiple different
aspects of business indicators in two-sided marketplaces to optimize a search
experience. We demonstrate that many problems are difficult or impossible to
decompose down to credit assigned scores on individual documents, rendering
traditional methods inadequate. Instead, we express market-level metrics as
constraints and discuss to what degree multiple potentially conflicting metrics
can be tuned to business needs. We further explore the use of policy learners
in the form of Evolutionary Strategies to jointly optimize both group-level and
market-level metrics simultaneously, side-stepping traditional cascading
methods and manual interventions. We empirically evaluate the effectiveness of
the proposed method on Etsy data and demonstrate its potential with insights.

Information Retrieval;Human-Computer Interaction;Machine Learning;Machine Learning