Formal models for expert finding in enterprise corpora

By Krisztian Balog, Leif Azzopardi, Maarten de Rijke. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2006

700 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Expertise modeling for matching papers with reviewers (2007, methodology): “This model is drawn from Petkova and Croft (2006), and is similar to Model 1 from Balog et al. (2006). We follow Petkova and Croft in setting λ = 0.”
Cross-domain collaboration recommendation (2012, background): “Our work is also related to expert finding [3, 30, 36] and expertise matching [23, 33].”
Explicit Semantic Ranking for Academic Search via Knowledge Graph Embedding (2017, background): “Other research on academic graphs includes the extraction and disambiguation of authors, integration of different publication resources [21], and expert finding [2, 7, 29].”
Social Network Analysis and Mining for Business Applications (2011, methodology): “A refinement of this approach is shown by Balog et al. [2006] who consider the relevance of documents for a query, so that not all documents authored by a person are considered equally.”
Learning joint query interpretation and response ranking (2012, background): “Given the success of generative techniques in corpus modeling [8], IR [41] and entity ranking [3, 4], it is natural to propose a generative language model approach to joint query interpretation and response ranking.”
Enhancing Expert Finding Using Organizational Hierarchies (2009, methodology): “[3], we use the content of members’ email to build an expertise profile for each of them.”