Probabilistic author-topic models for information discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Enhancing Expert Finding Using Organizational Hierarchies
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
The anatomy of a large-scale social search engine
Proceedings of the 19th international conference on World wide web
Discovering social media experts by integrating social networks and contents
ADC '12 Proceedings of the Twenty-Third Australasian Database Conference - Volume 124
Expert group formation using facility location analysis
Information Processing and Management: an International Journal
| Hi-index | 0.00 |
Expert finding is a task of finding knowledgeable people on a given topic. State-of-the-art expertise retrieval algorithms identify matching experts based on analysis of textual content of documents experts are associated with. While powerful, these models ignore social structure that might be available. In this paper, we develop a Bayesian hierarchical model for expert finding that accounts for both social relationships and content. The model assumes that social links are determined by expertise similarity between candidates. We demonstrate the improved retrieval performance of our model over the baseline on a realistic data set.