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The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
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UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
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SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
ArnetMiner: extraction and mining of academic social networks
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Formal Models for Expert Finding on DBLP Bibliography Data
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
A mixture model for expert finding
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
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ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
CollabSeer: a search engine for collaboration discovery
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems
To better stand on the shoulder of giants
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SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Learning to predict reciprocity and triadic closure in social networks
ACM Transactions on Knowledge Discovery from Data (TKDD)
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Establishing a respectful and well-functional program committee (PC) consisting of capable PC members is one of the most important tasks for conference organizers. However, little research has been done for automatic recommendation of PC candidates. PC member finding is a complex task, which could be influenced by many factors such as the candidates' research interests' match with conference topics, the candidates' social closeness with PC chairs, the candidates' authoritativeness, as well as the candidates' publication history in the conference. To examine the importance of each feature, we build a dataset that consists of papers from four conferences: KDD, SIGIR, JCDL and GIS (2007-2011) and split it into the training and testing subsets based on the temporal information. The results show that: i) the publication history is the strongest indicator of being PC members; ii) recommendations based on the social closeness also produce reasonable good results; iii) recommend high authority researchers as PC members fails to predict the real PC because there are a large proportion of PC members who actually only have low authority values (we use the PageRank value in coauthor networks to simulate researcher's authority); and iv) applying simple linear combination of different features can make reasonable improvements.