eBay has announced its 4th Annual Machine Learning University Challenge with this year's focus on extracting relevant product data from listing titles.
eBay is pleased to announce its 4th Annual University Challenge in the space of Machine Learning on an e-commerce dataset...
...As in the last three years, the prize for this year’s winners will be a summer internship with eBay (this time for the 2023 summer) .
The problem we invite you to consider for this year is to build a model that can accurately extract and label the named entities in the dataset of item titles on eBay. Named Entities are the semantic strings/words/phrases that refer to people, brands, organizations, locations, styles, materials, patterns, product names, units of measure, clothing sizes, etc.
It's always interesting to see what problems eBay sets out for these young minds to solve, revealing areas where the tech-led reimagination of the company may be focusing next.
In past competitions, eBay asked students to help more accurately predict shipping delivery dates and grapple with structured data challenges like how to identify two or more listings as being for the same product by putting them into the same group.
As in past years, the prize students are competing for a summer internship at eBay, with this year's winners taking a spot in the summer 2023 internship program.
eBay’s internship program is a combination of real work experience plus a robust program giving interns exposure to various business verticals, executives and networking opportunities. The internship will also be an excellent opportunity for students to put their ML models into real use.
If you were running eBay's University Machine Learning Competition, what would be your top challenge for these students to solve? Let us know in the comments below!
My personal pick would be to use machine learning to identify and remove scam listings/flag hijacked accounts used for fraud based on common image and listing attributes - I'll even give them one brown 1974 Audi as a head start. 😉