Optimization of the results of a multilingual search engine using a fuzzy recommendation approach

Abstract

Search engines are now the main source for information retrieval due to the hugeexpansion of data on the internet over the last ten years. Providing users with the mostrelevant results for their queries poses a significant challenge for search engines.Semantic search engines, which go beyond traditional keyword-based searches, haveappeared as advanced information retrieval systems to address this problem. Thesesearch engines produce more precise and pertinent search results because theyunderstand the meanings of words and their relationships. They play a pivotal role inmanaging the vast amount of internet data, with a primary aim of enhancing searchprecision and user satisfaction. However, improving search precision remains as animportant goal for natural language processing researchers. The main objective of ourresearch is to improve the search engine results. We present a novel approach formeasuring the similarity between a user’s query and a list of documents within a searchengine. This approach provides a new fuzzy recommendation system using a syntacticand semantic similarity. Our results indicate that our method outperforms severalexisting approaches from the literature, achieving a high level of accuracy.