Wildberries Enhances Product Search by Image

Posted by By Dan Taylor 1 Min Read

Wildberries has improved its image-based product search feature by updating the underlying neural network. The new system now considers the texture and small details of products to deliver more accurate results.

This update stems from training the neural network using millions of popular products from the marketplace. Wildberries claims that the accuracy of the search tool is now three times better than the previous version. The company initially introduced photo-based search in 2019, though the original neural network had been trained on a much smaller dataset.

With this new feature, users can upload an image, and the system will identify the specific product model or offer similar alternatives. The mobile app also allows users to highlight the area of interest in the photo, making searches even more precise.

Additionally, Wildberries recently launched a subscription service called “WB Club,” which offers access to a special section with exclusive discounts. The company’s founder, Tatyana Kim (Bakalchuk), has also hinted at upcoming services that will be introduced soon.

Source: vc.ru

Dan Taylor
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Dan Taylor is the founder and editor of International Search Insights (ISI), the publication behind ReadIsi, and a Partner and Head of Innovation (Search & AI) at SALT.agency. He has worked in search for more than a decade, in-house and agency-side, and is regarded as one of the West’s leading voices on search engines beyond Google, including Yandex, Baidu, Naver and Cốc Cốc. On ReadIsi, Dan covers the engines and AI answer platforms that English-language coverage tends to miss: Baidu and ERNIE, Yandex and Alice AI, Naver, Cốc Cốc, Shenma, Sogou, WeChat and Weibo search, plus DeepSeek and Alibaba’s Qwen. He also keeps dated records of documented changes, such as the Yandex, Baidu and Naver algorithm update histories and the DeepSeek, ERNIE and Qwen model update histories. His approach is to take learnings from global search engines and apply logic with a high evidence threshold. A claim needs a source, patterns seen in one market are treated as a hypothesis until they are checked in another, and where the evidence is thin he says so. For brands, that means advice built on what can be shown, with measurement that separates being cited from being chosen. Dan is credited with coining the term “edge SEO” and won the inaugural TechSEO Boost research competition in 2018 for his work on CDN and edge changes for SEO. In 2026 he was credited with coining “Infinite Tail” to describe how keyword research is changing with AI search. He is the author of AI Search Marketing For The Travel Sector, writes for Search Engine Journal, Search Engine Land and Travel Marketing Guy, and has advised technology, SaaS, travel and enterprise ecommerce brands, including Cloudflare, GitLab and Airbnb. He has given multiple talks and panels at the International Search Summit in Barcelona, and has also spoken at Optimization Moscow, brightonSEO, Google Search Central Live Deep Dive Asia in Bangkok, Athens SEO and the Asia SEO Virtual Summit 2026, where his talk looked at how Baidu, Naver, DeepSeek, Tencent, Cốc Cốc and Yandex built their own AI answers.
Founder & Editor