The Journal of Machine Learning Research
Knowledge sharing and yahoo answers: everyone knows something
Proceedings of the 17th 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 to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
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
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Learning to Rank for Information Retrieval and Natural Language Processing
Learning to Rank for Information Retrieval and Natural Language Processing
Question routing in community question answering: putting category in its place
Proceedings of the 20th ACM international conference on Information and knowledge management
A classification-based approach to question routing in community question answering
Proceedings of the 21st international conference companion on World Wide Web
Finding expert users in community question answering
Proceedings of the 21st international conference companion on World Wide Web
Dual role model for question recommendation in community question answering
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Joint relevance and answer quality learning for question routing in community QA
Proceedings of the 21st ACM international conference on Information and knowledge management
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This paper focuses on the problem of Question Routing (QR) in Community Question Answering (CQA), which aims to route newly posted questions to the potential answerers who are most likely to answer them. Traditional methods to solve this problem only consider the text similarity features between the newly posted question and the user profile, while ignoring the important statistical features, including the question-specific statistical feature and the user-specific statistical features. Moreover, traditional methods are based on unsupervised learning, which is not easy to introduce the rich features into them. This paper proposes a general framework based on the learning to rank concepts for QR. Training sets consist of triples (q, asker, answerers) are first collected. Then, by introducing the intrinsic relationships between the asker and the answerers in each CQA session to capture the intrinsic labels/orders of the users about their expertise degree of the question q, two different methods, including the SVM-based and RankingSVM-based methods, are presented to learn the models with different example creation processes from the training set. Finally, the potential answerers are ranked using the trained models. Extensive experiments conducted on a real world CQA dataset from Stack Overflow show that our proposed two methods can both outperform the traditional query likelihood language model (QLLM) as well as the state-of-the-art Latent Dirichlet Allocation based model (LDA). Specifically, the RankingSVM-based method achieves statistical significant improvements over the SVM-based method and has gained the best performance.