The Journal of Machine Learning Research
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Expertise networks in online communities: structure and algorithms
Proceedings of the 16th international conference on World Wide Web
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
Tapping on the potential of q&a community by recommending answer providers
Proceedings of the 17th ACM conference on Information and knowledge management
Learning to recommend questions based on user ratings
Proceedings of the 18th ACM conference on Information and knowledge management
Co-authorship networks in the digital library research community
Information Processing and Management: an International Journal - Special issue: Infometrics
The anatomy of a large-scale social search engine
Proceedings of the 19th international conference on World wide web
Modeling documents as mixtures of persons for expert finding
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Confucius and its intelligent disciples: integrating social with search
Proceedings of the VLDB Endowment
Predicting web searcher satisfaction with existing community-based answers
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
I want to answer; who has a question?: Yahoo! answers recommender system
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Survey of social search from the perspectives of the village paradigm and online social networks
Journal of Information Science
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Community based question and answering (cQA) services provide a convenient way for online users to share and exchange information and knowledge, which is highly valuable for information seeking. User interest and dedication act as the motivation to promote the interactive process of question and answering. In this paper, we aim to address a key issue about cQA systems: routing newly asked questions to appropriate users that may potentially provide answer with high quality. We incorporate answer quality and answer content to build a probabilistic question routing model. Our proposed model is capable of 1) differentiating and quantifying the authority of users for different topic or category; 2) routing questions to users with expertise. Experimental results based on a large collection of data from Wenwen demonstrate that our model is effective and has promising performance.