Relevance weighting for query independent evidence
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
Proximity-based document representation for named entity retrieval
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
A study of the relationship between ad hoc retrieval and expert finding in enterprise environment
Proceedings of the 10th ACM workshop on Web information and data management
Integrating multiple document features in language models for expert finding
Knowledge and Information Systems
Learning to rank for expert search in digital libraries of academic publications
EPIA'11 Proceedings of the 15th Portugese conference on Progress in artificial intelligence
Foundations and Trends in Information Retrieval
| Hi-index | 0.00 |
We argue that expert finding is sensitive to multiple document features in an organization, and therefore, can benefit from the incorporation of these document features. We propose a unified language model, which integrates multiple document features, namely, multiple levels of associations, PageRank, indegree, internal document structure, and URL length. Our experiments on two TREC Enterprise Track collections, i.e., the W3C and CSIRO datasets, demonstrate that the natures of the two organizational intranets and two types of expert finding tasks, i.e., key contact finding for CSIRO and knowledgeable person finding for W3C, influence the effectiveness of different document features. Our work provides insights into which document features work for certain types of expert finding tasks, and helps design expert finding strategies that are effective for different scenarios.