eBay once again turned to university students to help it solve real-world problems. This week, the company announced the winners of its 4th Annual Machine Learning Challenge.
Last year, eBay tasked students with helping it improve the delivery estimates it displays to show shoppers approximately how long it will take for them to receive their items.
This year, it tasked students with helping it analyze eBay listing titles in order to help it classify listings. eBay explained:
“This year, applicants were given the challenge of building a model that can accurately extract and label the named entities in the dataset of item titles on eBay. Those “named entities” might include brands, locations, styles, product names, colors, materials, sizes, and other semantic strings, words and phrases that can help classify an item.”
eBay said the students used a machine-learning process called Named Entity Recognition (NER) and said applicants tackled the very real-world challenge any ecommerce platform faces: “how do we extract structured data from unstructured sources like listings?”
eBay announced two winners out of the 591 teams (consisting of 900 students from 157 colleges) that entered this year: Rupashi Sangal and Sanjayan Pradeep Kumar Sreekala, students at the University of California, San Diego
One of the attractions in entering the contest, according to Sangal, was the opportunity to work with a real-world dataset. eBay provided the students with a data set of eBay women’s handbag listings.
Sangal and Sreekala have accepted internships with eBay’s Structured Data Applied Research team in San Jose for the summer of 2023.
More information about this year’s eBay Machine Learning Challenge for University Students is available on the eBay corporate blog.