AI for online stores: smart search and recommendations

Any buyer wants to find the right product quickly and easily. If the search in an online store is inconvenient, they may go to competitors. AI search for online stores makes this process simpler and more convenient: the algorithm understands queries even without precise wording, while personalized recommendations help users find the right product and related services. This saves customers’ time and increases sales.

How AI Product Search Works

AI for eCommerce is a real breakthrough, as it makes search “smart” and flexible. It goes beyond exact word matching — analyzing the meaning of a query, product characteristics, and customer behavior. As a result, the user receives the most relevant results, even if the query was entered inaccurately.

How AI search works for an online store:

• understanding the query — the neural network corrects typos, identifies entities, and determines intent (to buy, compare, or learn a size); modern systems use large language models (LLMs) trained to work with text. They can rephrase queries, find synonyms, and take into account the session context, which is especially important if the user has already viewed similar products;
• creating embeddings — the query and textual (and sometimes visual) product descriptions are converted into vectors, allowing matches to be found not only by words but also by meaning. This vector model forms the foundation of “semantic” search;
• fast search — a hybrid approach is typically used: lexical search (exact matches) + semantic search (meaning-based matches). This model allows both precise results (article number, brand) and similar products (“lightweight running shoes”) to be found;
• re-ranking and personalization — neural models and business rules reorder the results based on clicks, conversions, and personal data. This creates the final product ranking displayed on the screen.

Thus, smart search enables an online store not just to “show products,” but to deliver truly useful and personalized results.

Recommendation Algorithms for Shoppers

Personalized AI product recommendations are an essential part of modern eCommerce. They help users find the right products faster and discover new ones, while businesses benefit from higher conversion rates and average order values. These recommendations are powered by algorithms that analyze product data, user behavior, and session context.

There are several approaches to building personalized recommendations when developing an online store:

• collaborative filtering — algorithms compare the behavior of different users, analyze their preferences and viewed items, and then recommend those items to users with similar interests; this method is effective but may face the “cold start” problem when there’s not enough data on new users or recently added products;
• content-based recommendations — focus on product attributes such as description, brand, and category to show similar items;
• sequential models — the algorithm considers the sequence of actions within a session and adjusts to the user’s current intentions;
• hybrid approaches — combine several methods at once, compensating for the weaknesses of individual models and providing more accurate recommendations.

Implementing such systems transforms a regular catalog into a personalized showcase. The customer sees offers tailored specifically to them, while the store gains more sales and satisfied clients thanks to a convenient and “intelligent” interface.

Automatic Query Completion

Autocomplete helps users find products faster by suggesting input options while they type. This reduces the number of typos and increases purchase likelihood when suggestions are relevant and personalized.

There are several approaches to implementing autocomplete on a website:

• prefix search — the algorithm suggests options as soon as the user begins typing; for example, entering “sne” might trigger “sneakers,” “men’s sneakers,” or “running sneakers.” Special “indexes” are created in the database to make suggestions appear instantly;
• N-gram indexing — the word is divided into small parts so that the system can find matches even from partial inputs (“sneak”); this is useful for incomplete or imprecise typing;
• error correction — the algorithm guesses what the user meant even if there’s a typo. For instance, typing “snakers” will still display the correct “sneakers”;
• semantic autocomplete — the system suggests not only by letters but also by meaning. For example, typing “run” might prompt “running shoes” or “sports shorts,” even if those words don’t match exactly;
• personalization — suggestions take into account what the user has already viewed or purchased, offering products that are likely to interest them.

Modern online stores often combine these methods. This approach not only improves the user experience but also increases click-through rates and search conversions.

Examples of AI Search in E-commerce

AI search has long been effectively used by major eCommerce platforms. Here are some examples:

• AliExpress, Temu — choose a product photo in the catalog and click the camera icon. The neural network first finds similar products with different prices for comparison and then shows related items;
• eBay — upload an image of the desired product. The system converts it into a vector and finds matching options even without a precise text description;
• Amazon — the neural network takes into account purchase history, clicks, and commercial signals to show products that the user is most likely to buy.

Although these examples highlight individual features, in reality, these platforms combine multiple approaches that work effectively together.

How to Implement AI Search on Your Website

Want to integrate AI search into your online store? Contact Megasite, an IT company based in Kyiv and operating across Ukraine. We’ve been developing websites for over 10 years and effectively implement smart technologies that help customers quickly find the right products and increase sales.