Information Retrieval Model with Query Expansion and User Preference Profile

Understanding the user's search intention enables identifying and extracting the most relevant and personalized search results from the available information, according to the user's needs. This paper proposes an algorithm for relevant information retrieval that combines user preferences profile and query expansion to get relevant and personalized search results. The information retrieval process is validated using Precision, Recall and Mean Average Precision (MAP) metrics applied to a dataset that contains the standardized documents and preferences profiles. The results allowed us to demonstrate that the algorithm improves the information retrieval process by finding documents with better quality and greater relevance to the users' needs.