On interactive learning-to-rank for IR: Overview, recent advances, challenges, and directions

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• 作者：Rodrigo Tripodi Calumby^a ; ^c ; ^{rtcalumby@ecomp.uefs.br" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Marcos André ; Gonç ; alves^b ; ^{mgoncalv@dcc.ufmg.br" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Ricardo da Silva Torres^c ; ^{rtorres@ic.unicamp.br" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Interactive retrieval ; Learning-to-rank ; Relevance feedback ; Multimedia retrieval ; Effectiveness evaluation ; User behavior
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：5 October 2016
• 年：2016
• 卷：208
• 期：Complete
• 页码：3-24
• 全文大小：1398 K

文摘

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.