The Journal of Machine Learning Research
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
Finding experts in community-based question-answering services
Proceedings of the 14th ACM international conference on Information and knowledge management
Knowledge sharing and yahoo answers: everyone knows something
Proceedings of the 17th international conference on World Wide Web
Tapping on the potential of q&a community by recommending answer providers
Proceedings of the 17th ACM conference on Information and knowledge management
Probabilistic question recommendation for question answering communities
Proceedings of the 18th international conference on World wide web
Routing Questions to the Right Users in Online Communities
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Learning optimal ranking with tensor factorization for tag recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Reciprocal rank fusion outperforms condorcet and individual rank learning methods
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
The anatomy of a large-scale social search engine
Proceedings of the 19th international conference on World wide web
Routing questions to appropriate answerers in community question answering services
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Predicting best answerers for new questions in community question answering
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Survey of social search from the perspectives of the village paradigm and online social networks
Journal of Information Science
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Community Question Answering (CQA) service which enables users to ask and answer questions have emerged popular on the web. However, lots of questions usually can't be resolved by appropriate answerers effectively. To address this problem, we present a novel approach to recommend users who are most likely to be able to answer the new question. Differently with previous methods, this approach utilizes the inherent semantic relations among asker-question-answerer simultaneously and perform the Answerer Recommendation task based on tensor factorization. Experimental results on two real-world CQA dataset show that the proposed method is able to recommend appropriate answerers for new questions and outperforms other state-of-the-art approaches.