Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
The Journal of Machine Learning Research
Expertise identification using email communications
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
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
Mining for proposal reviewers: lessons learned at the national science foundation
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Hierarchical Language Models for Expert Finding in Enterprise Corpora
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Generalized comparison of graph-based ranking algorithms for publications and authors
Journal of Systems and Software
Hits on question answer portals: exploration of link analysis for author ranking
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Expertise modeling for matching papers with reviewers
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A CDD-based formal model for expert finding
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Modeling multi-step relevance propagation for expert finding
Proceedings of the 17th ACM conference on Information and knowledge management
A language modeling framework for expert finding
Information Processing and Management: an International Journal
A Topic Modeling Approach and Its Integration into the Random Walk Framework for Academic Search
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Social influence analysis in large-scale networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Hidden Topic Analysis Based Formal Framework for Finding Experts in Metadata Corpus
ICIS '09 Proceedings of the 2009 Eigth IEEE/ACIS International Conference on Computer and Information Science
ExpertRank: An Expert User Ranking Algorithm in Online Communities
NISS '09 Proceedings of the 2009 International Conference on New Trends in Information and Service Science
PageRank for ranking authors in co-citation networks
Journal of the American Society for Information Science and Technology
Co-authorship networks in the digital library research community
Information Processing and Management: an International Journal - Special issue: Infometrics
Popular and/or prestigious? Measures of scholarly esteem
Information Processing and Management: an International Journal
Discovering author impact: A PageRank perspective
Information Processing and Management: an International Journal
Topic-based PageRank on author cocitation networks
Journal of the American Society for Information Science and Technology
A user-oriented model for expert finding
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
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Expert finding is of vital importance for exploring scientific collaborations to increase productivity by sharing and transferring knowledge within and across different research areas. Expert finding methods, including content-based methods, link structure-based methods, and a combination of content-based and link structure-based methods, have been studied in recent years. However, most state-of-the-art expert finding approaches have usually studied candidates' personal information (e.g. topic relevance and citation counts) and network information (e.g. citation relationship) separately, causing some potential experts to be ignored. In this paper, we propose a topical and weighted factor graph model that simultaneously combines all the possible information in a unified way. In addition, we also design the Loopy Max-Product algorithm and related message-passing schedules to perform approximate inference on our cycle-containing factor graph model. Information Retrieval is chosen as the test field to identify representative authors for different topics within this area. Finally, we compare our approach with three baseline methods in terms of topic sensitivity, coverage rate of SIGIR PC (e.g. Program Committees or Program Chairs) members, and Normalized Discounted Cumulated Gain scores for different rankings on each topic. The experimental results demonstrate that our factor graph-based model can definitely enhance the expert-finding performance.