Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)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 ov...
This paper is concerned with the generaliza-tion ability of learning to rank algorithms for informat...
Learning to Rank (LTR) is one of the current problems in Information Retrieval (IR) that attracts th...
International audienceModern Information Retrieval (IR) systems become more and more complex, involv...
Online learning to rank methods for IR allow retrieval systems to optimize their own performance dir...
During the past 10--15 years offline learning to rank has had a tremendous influence on information ...
This paper is concerned with learning to rank for information retrieval (IR). Ranking is the central...
Ranking a set of documents based on their relevances with respect to a given query is a central prob...
Due to the fast growth of the Web and the difficulties in finding desired information, efficient and...
In this article we give an overview of our recent work on online learning to rank for information re...
As retrieval systems become more complex, learning to rank approaches are being developed to automat...
Abstract. As retrieval systems become more complex, learning to rank approa-ches are being developed...
In this paper we give an overview of and outlook on research at the intersection of information retr...
The amount of digital data we produce every day far surpasses our ability to process this data, and ...
One central problem of information retrieval (IR) is to determine which documents are relevant and w...
Information Retrieval (IR) is deal as the interface between the information handler and the framewor...
This paper is concerned with the generaliza-tion ability of learning to rank algorithms for informat...
Learning to Rank (LTR) is one of the current problems in Information Retrieval (IR) that attracts th...
International audienceModern Information Retrieval (IR) systems become more and more complex, involv...
Online learning to rank methods for IR allow retrieval systems to optimize their own performance dir...
During the past 10--15 years offline learning to rank has had a tremendous influence on information ...
This paper is concerned with learning to rank for information retrieval (IR). Ranking is the central...
Ranking a set of documents based on their relevances with respect to a given query is a central prob...
Due to the fast growth of the Web and the difficulties in finding desired information, efficient and...
In this article we give an overview of our recent work on online learning to rank for information re...
As retrieval systems become more complex, learning to rank approaches are being developed to automat...
Abstract. As retrieval systems become more complex, learning to rank approa-ches are being developed...
In this paper we give an overview of and outlook on research at the intersection of information retr...
The amount of digital data we produce every day far surpasses our ability to process this data, and ...
One central problem of information retrieval (IR) is to determine which documents are relevant and w...
Information Retrieval (IR) is deal as the interface between the information handler and the framewor...
This paper is concerned with the generaliza-tion ability of learning to rank algorithms for informat...
Learning to Rank (LTR) is one of the current problems in Information Retrieval (IR) that attracts th...
International audienceModern Information Retrieval (IR) systems become more and more complex, involv...