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In recent years online advertising has become increasingly ubiquitous andeffective. Advertisements shown to visitors fund sites and apps that publishdigital content, manage social networks, and operate e-mail services. Givensuch large variety of internet resources, determining an appropriate type ofadvertising for a given platform has become critical to financial success.Native advertisements, namely ads that are similar in look and feel to content,have had great success in news and social feeds. However, to date there has notbeen a winning formula for ads in e-mail clients. In this paper we describe asystem that leverages user purchase history determined from e-mail receipts todeliver highly personalized product ads to Yahoo Mail users. We propose to usea novel neural language-based algorithm specifically tailored for deliveringeffective product recommendations, which was evaluated against baselines thatincluded showing popular products and products predicted based onco-occurrence. We conducted rigorous offline testing using a large-scaleproduct purchase data set, covering purchases of more than 29 million usersfrom 172 e-commerce websites. Ads in the form of product recommendations weresuccessfully tested on online traffic, where we observed a steady 9% lift inclick-through rates over other ad formats in mail, as well as comparable liftin conversion rates. Following successful tests, the system was launched intoproduction during the holiday season of 2014.

E-commerce in Your Inbox: Product Recommendations at Scale