eBay has announced their 3rd Annual eBay University Challenge and this year the challenge is in the space of Machine Learning on an ecommerce dataset.
Last year’s competition attracted 600 students from almost 200 universities with the problem posed being Building a Product Catalogue, addressing the conundrum of how to identify two or more listings as being for the same product by putting them into the same group
The problem statement eBay are inviting students to consider this year, is how to build a model that can accurately predict delivery dates for items sold on eBay, given a dataset of pertinent shipping information. The accuracy of shipping estimates plays a significant role in providing a hassle-free and trusty customer experience. However, this particular area has not received enough attention within the machine learning community despite its growing importance in the new online world. eBay want to change that and are the eBay University Challenge aims to attract some of the brightest brains to focus on the problem.
“The question we invite you to address is to estimate the delivery date of shipments of online purchases. The shipments come from a diverse set of sellers on eBay, ranging from people selling items from their households to large business sellers.
The journey of a package from a seller to buyer is made up of 2 parts. The first part is the handling time, which covers the time taken by the seller to package the item until it is handed over to the carrier. The second part is the transit time, which is the time taken by the carrier to deliver the package.”
– eBay University Challenge
As in previous years, the prize for this year’s eBay University Challenge winners will be a summer internship with eBay (this time for the 2022 summer).
