  Correctly pricing products or services in an online marketplace presents a
challenging problem and one of the critical factors for the success of the
business. When users are looking to buy an item they typically search for it.
Query relevance models are used at this stage to retrieve and rank the items on
the search page from most relevant to least relevant. The presented items are
naturally "competing" against each other for user purchases. We provide a
practical two-stage model to price this set of retrieved items for which
distributions of their values are learned. The initial output of the pricing
strategy is a price vector for the top displayed items in one search event. We
later aggregate these results over searches to provide the supplier with the
optimal price for each item. We applied our solution to large-scale search data
obtained from Airbnb Experiences marketplace. Offline evaluation results show
that our strategy improves upon baseline pricing strategies on key metrics by
at least +20% in terms of booking regret and +55% in terms of revenue
potential.
