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
A comparative study of methods for estimating query language models with pseudo feedback
Proceedings of the 18th ACM conference on Information and knowledge management
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Recommending program committee candidates for academic conferences
Proceedings of the 2013 workshop on Computational scientometrics: theory & applications
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Current search systems are designed to find relevant articles, especially topically relevant ones, but the notion of relevance largely depends on search tasks. We study the specific task that scientists are searching for worth-reading articles beneficial for their research. Our study finds: users' perception of relevance and preference of reading are only moderately correlated; current systems can effectively find readings that are highly relevant to the topic, but 36% of the worth-reading articles are only marginally relevant or even non-relevant. Our system can effectively find those worth-reading but marginally relevant or non-relevant articles by taking advantages of scientists' recommendations in social websites.