AI-Driven Search Bar for E-commerce
This development focused on improving the search bar for the client's online store using artificial intelligence and machine learning. The main advantages of the provided solution are its autonomous operation and data security, since it easily integrates into the customer's existing search system and does not rely on cloud services for data storage.
Business Challenge
Business Challenge
Our goal was to develop a powerful search bar for quick and accurate product searches on the website of the online store for household appliances – KTC.UA.
Due to the use of dialects in query typing and the multilingual environment in Ukraine, there was a problem of mismatched search results with the client's needs. This, in turn, led to a decrease in sales. Our goal was to create a ready-to-use product that would provide customers with precise and relevant search results based on the availability in the product database.
Solution Overview
Solution Overview
Our system uses modern methods of natural language processing and artificial intelligence to optimize the search field. Through textual data analysis, we are improving the indexing of large volumes of information and ranking algorithms,enabling fast and accurate search results. We consider synonyms, perform stemming and character mapping to recognize different forms of the same word and reduce them to common terms. The Qudata team processed stop words and corrected the errors in writing and keyboard layout. We also conducted normalization of the client's product database, created synonym connections, and implemented a new search system based on our algorithms and data.
As a result of our work, customers can find their desired products more quickly and accurately. We have also improved website navigation unrelated to specific products, such as searching for information about payment, delivery, or promotions.
Technical Details
Technical Details
The search system was implemented on the basis of Elasticsearch, which allows searching, analyzing, and processing large volumes of data. After testing the prototype, it was decided to develop the final solution on the server platform – Node.js, to ensure efficient storage and access to information.Through close collaboration with the client's IT department, this solution was successfully implemented with the subsequent transfer of the necessary set of data, algorithms, and documentation to the client, enabling them to independently maintain and further develop this technology in the future.
Technology Stack
elasticsearch
NodeJS
