Modern Methods Of Determining Relevance In Web Search Systems - Eshko'ziyeva, Rayhona (International Conference on Global Trends and Innovations in Multidisciplinary Research, 2026)
This article examines modern methods for relevance determination in web search engines. The analysis covers classical approaches such as TF-IDF and BM25, as well as neural network-based techniques including BERT, dense retrieval, and multi-vector models. Semantic search, Learning to Rank (LTR) technologies, user behavioral signals, and multimodal search systems are discussed. The effectiveness of methods is evaluated based on the practical experience of major systems — Google, Bing, and Elasticsearch.