ANALYSIS OF EXISTING INTELLIGENT SEARCH ALGORITHMS

The article presents a comprehensive analysis of existing intelligent search algorithms to determine their effectiveness and application areas. Traditional statistical methods TF-IDF and BM25, text normalization methods including Porter, Lovins and Snowball stemming algorithms, as well as error correction approaches based on Levenshtein distance are investigated. Modern semantic search technologies using word vector representation models and transformer models are examined. The advantages and disadvantages of each approach are analyzed, and optimal areas of their application are determined. It is established that hybrid solutions combining traditional statistical methods with modern deep learning technologies demonstrate the best results. The research results can be used for developing effective search systems and optimizing existing solutions.