Improving the effectiveness of information retrieval with local context analysis
ACM Transactions on Information Systems (TOIS)
The information-seeking practices of engineers: searching for documents as well as for people
Information Processing and Management: an International Journal
Active feedback in ad hoc information retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
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
Hierarchical Language Models for Expert Finding in Enterprise Corpora
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Using relevance feedback in expert search
ECIR'07 Proceedings of the 29th European conference on IR research
Non-local evidence for expert finding
Proceedings of the 17th ACM conference on Information and knowledge management
Key blog distillation: ranking aggregates
Proceedings of the 17th ACM conference on Information and knowledge management
Studying Query Expansion Effectiveness
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
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
Improved latent concept expansion using hierarchical markov random fields
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
The influence of the document ranking in expert search
Information Processing and Management: an International Journal
Linked-data based suggestion of relevant topics
Proceedings of the 7th International Conference on Semantic Systems
A Survey of Automatic Query Expansion in Information Retrieval
ACM Computing Surveys (CSUR)
Phrase pair classification for identifying subtopics
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
Foundations and Trends in Information Retrieval
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
Finding co-solvers on twitter, with a little help from linked data
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Selecting effective expansion terms for diversity
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
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Pseudo-relevance feedback, or query expansion, has been shown to improve retrieval performance in the adhoc retrieval task. In such a scenario, a few top-ranked documents are assumed to be relevant, and these are then used to expand and refine the initial user query, such that it retrieves a higher quality ranking of documents. However, there has been little work in applying query expansion in the expert search task. In this setting, query expansion is applied by assuming a few top-ranked candidates have relevant expertise, and using these to expand the query. Nevertheless, retrieval is not improved as expected using such an approach. We show that the success of the application of query expansion is hindered by the presence of topic drift within the profiles of experts that the system considers. In this work, we demonstrate how topic drift occurs in the expert profiles, and moreover, we propose three measures to predict the amount of drift occurring in an expert's profile. Finally, we suggest and evaluate ways of enhancing query expansion in expert search using our new insights. Our results show that, once topic drift has been anticipated, query expansion can be successfully applied in a general manner in the expert search task.