Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: With the amount and variety of information available on digital repositories, ansring 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.  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.
['Rodrigo Tripodi Calumby', 'Marcos André Gonçalves', 'Ricardo da Silva Torres']
I am writing a research paper on a idea On interactive learning-to-rank for IR: Overview, recent advances, challenges, and directions
[{"from": "human", "value": "I am writing a research paper on a idea On interactive learning-to-rank for IR: Overview, recent advances, challenges, and directions"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: With the amount and variety of information available on digital repositories, ansring 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.  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."}]