Unsupervised Effectiveness Estimation through Rank Based Measures

• Grant number: 23/01828-4
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: April 01, 2023
• End date: March 31, 2024
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: Daniel Carlos Guimarães Pedronette
• Grantee: Thiago César Castilho Almeida
• Host Institution: Instituto de Geociências e Ciências Exatas (IGCE). Universidade Estadual Paulista (UNESP). Campus de Rio Claro. Rio Claro , SP, Brazil
• Associated research grant: 18/15597-6 - Aplication and investigation of unsupervised learning methods in retrieval and classification tasks, AP.JP2

Abstract

In an unsupervised scenario, where no labeled data is available, part of the problem of improving retrieval results consis in identifying high and low effective ranked lists. Therefore, estimating the effectiveness of retrieved results without the need of user intervention is of critical importance. The quality of retrieved results for a given query may be used to improve the search system automatically. For example, considering that the effectiveness of results for a given query is known, the CBIR system may perform re-ranking or may support relevance feedback sessions for improving the quality of low-effective queries. In addition, a greater relevance can be assigned to high-effective queries, and that information may be used to tune searching models aiming at improving the results associated with future queries. In this research project, we aim at investiganting unsupervised effectiveness estimation measures based on ranking information.