A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Expertise recommender: a flexible recommendation system and architecture
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Recommending expertise in an organizational setting
CHI '99 Extended Abstracts on Human Factors in Computing Systems
DEMOIR: A Hybrid Architecture for Expertise Modeling and Recommender Systems
WETICE '00 Proceedings of the 9th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises
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
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Expertise recommenders that help in tracing expertise rather than documents start to apply some advanced information retrieval techniques. This paper introduces an odds ratio model to model expert entities for expert finding. This model applies odds ratio instead of raw probability to use language modeling techniques. A raw language model that uses prior probability for smoothing has a tendency to boost up "common" experts. In such a model the score of a candidate expert increases as its prior probability increases. Therefore, the system would trend to suggest people who have relatively large prior probabilities but not the real experts. While in the odds ratio model, such a tendency is avoided by applying an inverse ratio of the prior probability to accommodate "common" experts. The experiments on TREC test collections shows the odds ratio model improves the performance remarkably.