eBay 2024 University Machine Learning Competition Application Deadline

Get ready for some exciting news: eBay is back with its 6th annual University Machine Learning Competition, focusing on an e-commerce dataset. The 2023 competition was a huge hit with students, and we want to give a big shoutout to all who participated – you rock! We’re excited to see the buzz this year’s challenge will generate!

Now, for the juicy part: The winners get a chance to snag a summer internship with eBay for 2025! Yep, you heard that right. We’re always on the lookout for top talent, and our intern program could be your ticket to full-time awesomeness.

eBay listens! Last year, some of our awesome challenge participants suggested we dive into the Motors scene 🚗. Well, guess what? We’re making it happen this year, and it’s going to be epic! 🌟

Here’s the scoop: We’re challenging you to dive deep into the world of parts and accessories for motor vehicles. Your mission? Build a model that’s on point at figuring out which vehicle can rock a specific part or accessory. We’re talking about “Fitment” – it’s the secret sauce that tells you if a part or accessory will mesh with your ride. 🧩🚘

Think you’ve got what it takes to nail the Fitment game? Show us what you’ve got, and let’s make this competition one for the books!

Fitment can be determined at several levels. For example, one might say a part is made for Honda vehicles, or for the Honda Civic model line, or for Honda Civic model years 2005-2019, or for Honda Civic model years 2005-2019 LX with a 2.0 liter engine. This challenge will be for fitment at the “Year Make Model” level (“Make” is an industry term for manufacturer or brand). Fitment can be seen as a machine learning problem aimed at extracting such compatibility information from text or images. In e-commerce, fitment plays a crucial role in processing listing titles, descriptions, listing specifics, queries, and reviews, or any context where the extraction of compatibility information from unstructured text is required. This year’s challenge focuses on data extraction from listings.

To reflect eBay’s wide-ranging collection of parts and accessories for motor vehicles, this year’s challenge pulls data from various categories within eBay Motors. We’re tapping into listings that span different groups of parts and accessories, ensuring a diverse and inclusive dataset for our competition. 🚗💨

After a signed data use agreement is in place, student teams from universities will be granted access to our dataset, enabling them to tackle a genuine e-commerce challenge. We are excited about the prospect of this real-world data sparking a deeper interest in the e-commerce business among students and our hope is to inspire innovative solutions to intricate challenges that can yield beneficial outcomes for both the community and the broader industry.