Improving Search Query Accuracy for Specialized Websites Through Intelligent Text Correction and Reconstruction Models

Abstract

In the digital era, the need for precise and efficient search operations is paramount as users increasingly rely on online resources to access specific information. However, search accuracy is often hindered by errors in user queries, such as incomplete or degraded input. Errors in search queries can reduce both the precision and speed of search results, making error correction a key factor in enhancing the user experience. This paper addresses the challenge of improving search performance through query error correction. We propose a novel methodology and architecture aimed at optimizing search results across thematic websites, such as those for universities, hospitals, or tourism agencies. The proposed solution leverages an intelligent model based on Gated Recurrent Units (GRUs) and Bahdanau Attention mechanisms to reconstruct erroneous or incomplete text in search queries. To validate our approach, we embedded the model in a prototype website consolidating data from multiple universities, demonstrating significant improvements in search accuracy and efficiency.

Keywords

• text correction
• machine learning
• search optimization