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
Hierarchical Language Models for Expert Finding in Enterprise Corpora
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Broad expertise retrieval in sparse data environments
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Socially augmenting employee profiles with people-tagging
Proceedings of the 20th annual ACM symposium on User interface software and technology
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Real-time automatic tag recommendation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Modeling multi-step relevance propagation for expert finding
Proceedings of the 17th ACM conference on Information and knowledge management
Proceedings of the 18th international conference on World wide web
Enhancing Expert Finding Using Organizational Hierarchies
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Usefulness of click-through data in expert search
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Determining expert profiles (with an application to expert finding)
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Modeling documents as mixtures of persons for expert finding
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Foundations and Trends in Information Retrieval
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In an enterprise search setting, there is a class of queries for which people, rather than documents, are desirable answers. However, presenting users with just a list of names of knowledgeable employees without any description of their expertise may lead to confusion, lack of trust in search results, and abandonment of the search engine. At the same time, building a concise meaningful description for a person is not a trivial summarization task. In this paper, we propose a solution to this problem by automatically tagging people for the purpose of profiling their expertise areas in the scope of the enterprise where they are employed. We address the novel task of automatic people tagging by using a machine learning algorithm that combines evidence that a certain tag is relevant to a certain employee acquired from different sources in the enterprise. We experiment with the data from a large distributed organization, which also allows us to study sources of expertise evidence that have been previously overlooked, such as personal click-through history. The evaluation of the proposed methods shows that our technique clearly outperforms state of the art approaches.