Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Proceedings of the 11th international conference on World Wide Web
The Journal of Machine Learning Research
Probabilistic author-topic models for information discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Topical link analysis for web search
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Expertise networks in online communities: structure and algorithms
Proceedings of the 16th international conference on World Wide Web
Discovering authorities in question answer communities by using link analysis
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Finding high-quality content in social media
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Fast collapsed gibbs sampling for latent dirichlet allocation
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Identifying authoritative actors in question-answering forums: the case of Yahoo! answers
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
Expert finding in question-answering websites: a novel hybrid approach
Proceedings of the 2010 ACM Symposium on Applied Computing
Automatic keyphrase extraction via topic decomposition
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Expert identification in community question answering: exploring question selection bias
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Routing questions to appropriate answerers in community question answering services
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Identifying topical authorities in microblogs
Proceedings of the fourth ACM international conference on Web search and data mining
Phrase-based translation model for question retrieval in community question answer archives
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Competition-based user expertise score estimation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
An analysis of the structure and dynamics of large-scale Q/A communities
ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
CQArank: jointly model topics and expertise in community question answering
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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In this paper, we address the problem of expert finding in community question answering (CQA). Most of the existing approaches attempt to find experts in CQA by means of link analysis techniques. However, these traditional techniques only consider the link structure while ignore the topical similarity among users (askers and answerers) and user expertise and user reputation. In this study, we propose a topic-sensitive probabilistic model, which is an extension of PageRank algorithm to find experts in CQA. Compared to the traditional link analysis techniques, our proposed method is more effective because it finds the experts by taking into account both the link structure and the topical similarity among users. We conduct experiments on real world data set from Yahoo! Answers. Experimental results show that our proposed method significantly outperforms the traditional link analysis techniques and achieves the state-of-the-art performance for expert finding in CQA.