Sponsored Link
Email This Post Email This Post

eBay Search Is a Testing Ground for Data Scientists

eBay is using technology to try and figure out what shoppers want, and it is reordering search results pages as it attempts to strike a balance between relevance and great deals, auctions and Buy It Now, and individual sellers and large retailers, according to eBay Technical Fellow David Goldberg.

In a post on its corporate blog on Monday, eBay explained how it uses machine learning technology to drive innovative new approaches to search experiences. But while that may sound good to the rest of the world, sellers may be wary of marketplaces that tinker with search, which is key to getting exposure to shoppers.

Even before eBay started touting its skills in artificial intelligence and machine learning, sellers were concerned over eBay’s use of an algorithm to influence which listings would appear higher on the search results page, something known as Best Match – the default sort order of listings. And that’s exactly where eBay is concentrating its testing.

In Monday’s post, eBay wrote, “The largest scale application of machine learning technology at eBay is currently Best Match, the algorithm used to optimize relevance for buyers during their shopping experiences. Best Match analyzes everything from item popularity to potential value to the buyer, to terms of service such as return policies. It is a powerful tool for surfacing deals.”

eBay Research Scientist Selcuk Kopru works on integrating machine learning with search on eBay. “We apply machine learning techniques to item-to-product matching, price prediction and item categorization tasks on eBay,” he said. “We also employ them for attribute extraction, generating the proper names of browse nodes, filtering product reviews and more. Machine learning helps us optimize the relevance of shoppers’ search and navigation experiences.”

Why aren’t keywords enough for optimized search experiences? “Search has moved well beyond simple keyword matching,” he said. “We have seen that extracting semantics from item titles and descriptions using machine learning algorithms has helped to improve relevance in our search experiences.”

Another eBay data scientist, Alex Cozzi, said, “We are also applying statistical learning to optimize the whole page: reacting to the users’ actions we can reorder and prioritize the content in the search page, providing search guidance or access to top deals and top products in appropriate context while minimizing distractions.”

Sellers have lived through a lot of tinkering with search technology, from the introduction of Best Match, to the Cassini re-platforming, and now machine learning.

With on-the-fly changes to the Best Match algorithm – eBay may decide to increase the importance of listings that offer a generous return policy, for instance – it’s a challenge to optimize listings. And what can be more unsettling to sellers is knowing that what they see in search results may be very different from what a potential shopper sees.

Let us know what you think.

Post a comment on the AuctionBytes Blog.

Ina Steiner on EmailIna Steiner on LinkedinIna Steiner on Twitter
Ina Steiner
Ina Steiner
Ina Steiner is co-founder and Editor of EcommerceBytes and has been reporting on ecommerce since 1999. She's a widely cited authority on marketplace selling and is author of "Turn eBay Data Into Dollars" (McGraw-Hill 2006). Her blog was featured in the book, "Blogging Heroes" (Wiley 2008). Follow her on Twitter at @ecommercebytes and send news tips to ina@ecommercebytes.com.