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Type: Artigo
Title: On Interactive Learning-to-rank For Ir: Overview, Recent Advances, Challenges, And Directions
Author: Calumby
Rodrigo Tripodi; Goncalves
Marcos Andre; Torres
Ricardo da Silva
Abstract: With the amount and variety of information available on digital repositories, answering complex user needs and personalizing information access became a hard task. Putting the user in the retrieval loop has emerged as a reasonable alternative to enhance search effectiveness and consequently the user experience. Due to the great advances on machine learning techniques, optimizing search engines according to user preferences has attracted great attention from the research and industry communities. Interactively learning-to-rank has greatly evolved over the last decade but it still faces great theoretical and practical obstacles. This paper describes basic concepts and reviews state-of-the-art methods on the several research fields that complementarily support the creation of interactive information retrieval (IIR) systems. By revisiting ground concepts and gathering recent advances, this article also intends to foster new research activities on IIR by highlighting great challenges and promising directions. The aggregated knowledge provided here is intended to work as a comprehensive introduction to those interested in IIR development, while also providing important insights on the vast opportunities of novel research. (C) 2016 Elsevier B.V. All rights reserved.
Subject: Interactive Retrieval
Relevance Feedback
Multimedia Retrieval
Effectiveness Evaluation
User Behavior
Editor: Elsevier Science BV
Rights: fechado
Identifier DOI: 10.1016/j.neucom.2016.03.084
Date Issue: 2016
Appears in Collections:Unicamp - Artigos e Outros Documentos

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