Yandex Maps Launches Personalized Recommendations With Neural Network Support

Yandex Maps has rolled out a new feature that gives users personalized recommendations directly within the app. The new system uses a neural network to suggest places tailored to a user’s preferences and context, such as time of day or whether they are at home or traveling.

Users can now see recommendations on the app’s main screen. These appear both as photo tags on the map and in a feed format under the search bar. The feed includes categories like “Eat,” “Drink,” “Entertainment,” and “Walk.”

Short descriptions generated by YandexGPT are now shown with many of the recommended places. These descriptions aim to help users quickly understand what to expect, such as “a mini-park in Japanese style” or “an entertainment center with a bar right in the pool.”

Smarter Choices Based On Personal Preferences

The updated recommendation system is powered by a neural network that analyzes city businesses and user preferences. It then suggests relevant places based on the user’s habits and current context.

Irina Gracheva, head of the Yandex Maps service, said the feature is designed to help users discover new places more easily. “Personalized recommendations in Maps will tell you where to go when you want to do something new,” she said.

Since launching the new system, Yandex Maps has seen increased interaction with businesses in the app. The monthly user base grew from 38 million to 46 million in 2024.

Available For Logged-In Users

Personalized recommendations are currently available to users who are logged in to their Yandex accounts. This allows the app to tailor suggestions better to each person. General recommendations for popular places in the city will be added for users who are not logged in.

This update follows other recent changes to Yandex Maps. In early April, the company updated its premium ad formats, and in March 2025, it introduced a new format for targeted advertising.

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.