In this paper, we propose a novel revenue model which jointly optimizes both for probability of purchase and profit. The model is tested on a recommendation module at Etsy.com, a two-sided marketplace for buyers and sellers. Notably, optimizing for profit, in addition to purchase probability, benefits not only the platform but also the sellers. We show that the proposed model outperforms several baselines by increasing offline metrics associated with both relevance and profit.

In this paper, we find that users implicitly reveal their session-specific budgets through the sequence of items they browse within that session. Specifically, we find that some users “browse down,” by purchasing the cheapest item among alternatives under consideration, others “browse up” (selecting the most expensive), and others ultimately purchase items around the middle.