Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
A citation-based system to assist prize awarding
ACM SIGMOD Record
Formal models for expert finding in enterprise corpora
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Generalized comparison of graph-based ranking algorithms for publications and authors
Journal of Systems and Software
A support vector method for optimizing average precision
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Proximity-based document representation for named entity retrieval
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Voting techniques for expert search
Knowledge and Information Systems
Overview of the INEX 2007 Entity Ranking Track
Focused Access to XML Documents
Modeling document features for expert finding
Proceedings of the 17th ACM conference on Information and knowledge management
Formal Models for Expert Finding on DBLP Bibliography Data
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Co-authorship networks in the digital library research community
Information Processing and Management: an International Journal - Special issue: Infometrics
Probabilistic models for expert finding
ECIR'07 Proceedings of the 29th European conference on IR research
Foundations and Trends in Information Retrieval
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The task of expert finding has been getting increasing attention in information retrieval literature. However, the current state-of-the-art is still lacking in principled approaches for combining different sources of evidence in an optimal way. This paper explores the usage of learning to rank methods as a principled approach for combining multiple estimators of expertise, derived from the textual contents, from the graph-structure with the citation patterns for the community of experts, and from profile information about the experts. Experiments made over a dataset of academic publications, for the area of Computer Science, attest for the adequacy of the proposed approaches.