Probabilistic models in information retrieval
The Computer Journal - Special issue on information retrieval
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
Discriminative models for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)
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
Voting for candidates: adapting data fusion techniques for an expert search task
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Linear feature-based models for information retrieval
Information Retrieval
Proximity-based document representation for named entity retrieval
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
ArnetMiner: extraction and mining of academic social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Non-local evidence for expert finding
Proceedings of the 17th ACM conference on Information and knowledge management
Modeling multi-step relevance propagation for expert finding
Proceedings of the 17th ACM conference on Information and knowledge management
A language modeling framework for expert finding
Information Processing and Management: an International Journal
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Probabilistic models for expert finding
ECIR'07 Proceedings of the 29th European conference on IR research
Integrating multiple document features in language models for expert finding
Knowledge and Information Systems
High quality expertise evidence for expert search
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Associating people and documents
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Modeling documents as mixtures of persons for expert finding
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Entity information management in complex networks
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
The influence of the document ranking in expert search
Information Processing and Management: an International Journal
Mimicking Web search engines for expert search
Information Processing and Management: an International Journal
Learning models for ranking aggregates
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Award prediction with temporal citation network analysis
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Foundations and Trends in Information Retrieval
Academic network analysis: a joint topic modeling approach
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Expertise retrieval in bibliographic network: a topic dominance learning approach
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Generative models such as statistical language modeling have been widely studied in the task of expert search to model the relationship between experts and their expertise indicated in supporting documents. On the other hand, discriminative models have received little attention in expert search research, although they have been shown to outperform generative models in many other information retrieval and machine learning applications. In this paper, we propose a principled relevance-based discriminative learning framework for expert search and derive specific discriminative models from the framework. Compared with the state-of-the-art language models for expert search, the proposed research can naturally integrate various document evidence and document-candidate associations into a single model without extra modeling assumptions or effort. An extensive set of experiments have been conducted on two TREC Enterprise track corpora (i.e., W3C and CERC) to demonstrate the effectiveness and robustness of the proposed framework.