The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Formal models for expert finding in enterprise corpora
Abstract
Searching an organization's document repositories for experts provides a cost effective solution for the task of expert finding. We present two general strategies to expert searching given a document collection which are formalized using generative probabilistic models. The first of these directly models an expert's knowledge based on the documents that they are associated with, whilst the second locates documents on topic, and then finds the associated expert. Forming reliable associations is crucial to the performance of expert finding systems. Consequently, in our evaluation we compare the different approaches, exploring a variety of associations along with other operational parameters (such as topicality). Using the TREC Enterprise corpora, we show that the second strategy consistently outperforms the first. A comparison against other unsupervised techniques, reveals that our second model delivers excellent performance.
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Cites
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A Language Modeling Approach to Information Retrieval
Ponte, J. M., & Croft, W. B. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130368 · Cited by 2,503
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A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval
Zhai, C., & Lafferty, J. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130377 · Cited by 1,537
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Zhai, C., & Lafferty, J. (2001) · Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/383952.384019 · Cited by 889
A hidden Markov model information retrieval system
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Using Language Models for Information Retrieval
Hiemstra, D. (2001) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.570441 · Cited by 448
Expertise recommender: a flexible recommendation system and architecture
McDonald, D. W., & Ackerman, M. S. (2000) · Proceedings of the 2000 ACM conference on Computer supported cooperative work · doi:10.1145/358916.358994 · Cited by 425
Expertise Browser: a quantitative approach to identifying expertise
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ArnetMiner: extraction and mining of academic social networks
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., & Su, Z. (2008) · Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1401890.1402008 · Cited by 2,231
Explicit Semantic Ranking for Academic Search via Knowledge Graph Embedding
Xiong, C., Power, R., & Callan, J. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052558 · Cited by 301
Expertise modeling for matching papers with reviewers
Mimno, D., & McCallum, A. (2007) · Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1281192.1281247 · Cited by 257
Today was a good day: The daily life of software developers
Meyer, A. N., Barr, E. T., Bird, C., & Zimmermann, T. (2021) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2019.2904957 · Cited by 123
Metadata
- Authors
- Balog, K., Azzopardi, L., & de Rijke, M.
- Venue
- Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
- Publisher
- ACM
- Published
- 2006-08-06
- Pages
- 43-50
- OpenAlex
- W2126226055
- Cited by
- 553
- References
- 15
- FWCI
- 32.31
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Balog, K., Azzopardi, L., & de Rijke, M. (2006). Formal models for expert finding in enterprise corpora. Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval. https://doi.org/10.1145/1148170.1148181
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
