Varun Bhagwan

E-commerce in Your Inbox: Product Recommendations at Scale

In recent years online advertising has become increasingly ubiquitous and effective. Advertisements shown to visitors fund sites and apps that publish digital content, manage social networks, and operate e-mail services. Given such large variety of internet resources, determining an appropriate type of advertising 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 not been a winning formula for ads in e-mail clients. In this paper we describe a system that leverages user purchase history determined from e-mail receipts to deliver highly personalized product ads to Yahoo Mail users. We propose to use a novel neural language-based algorithm specifically tailored for delivering effective product recommendations, which was evaluated against baselines that included showing popular products and products predicted based on co-occurrence. We conducted rigorous offline testing using a large-scale product purchase data set, covering purchases of more than 29 million users from 172 e-commerce websites. Ads in the form of product recommendations were successfully tested on online traffic, where we observed a steady 9% lift in click-through rates over other ad formats in mail, as well as comparable lift in conversion rates. Following successful tests, the system was launched into production during the holiday season of 2014.

Travel the World: Analyzing and Predicting Booking Behavior using E-mail Travel Receipts

Tourism industry has grown tremendously in the previous several decades. Despite its global impact, there still remain a number of open questions related to better understanding of tourists and their habits. In this work we analyze the largest data set of travel receipts considered thus far, and focus on exploring and modeling booking behavior of online customers. We extract useful, actionable insights into the booking behavior, and tackle the task of predicting the booking time. The presented results can be directly used to improve booking experience of customers and optimize targeting campaigns of travel operators.