Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Ordering by weighted number of wins gives a good ranking for weighted tournaments
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
PolyLens: a recommender system for groups of users
ECSCW'01 Proceedings of the seventh conference on European Conference on Computer Supported Cooperative Work
Expertise networks in online communities: structure and algorithms
Proceedings of the 16th international conference on World Wide Web
Recommending questions using the mdl-based tree cut model
Proceedings of the 17th international conference on World Wide Web
Introduction to Information Retrieval
Introduction to Information Retrieval
Routing Questions to the Right Users in Online Communities
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Group recommendation: semantics and efficiency
Proceedings of the VLDB Endowment
G-Finder: routing programming questions closer to the experts
Proceedings of the ACM international conference on Object oriented programming systems languages and applications
Question routing in community question answering: putting category in its place
Proceedings of the 20th ACM international conference on Information and knowledge management
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An online community consists of a group of users who share a common interest, background, or experience and their collective goal is to contribute towards the welfare of the community members. Question answering is an important feature that enables community members to exchange knowledge within the community boundary. The overwhelming number of communities necessitates the need for a good question routing strategy so that new questions gets routed to the appropriately focused community and thus get resolved. In this paper, we consider the novel problem of routing questions to the right community and propose a framework to select the right set of communities for a question. We begin by using several prior proposed features for users and add some additional features, namely language attributes and inclination to respond, for community modeling. Then we introduce two k nearest neighbor based aggregation algorithms for computing community scores. We show how these scores can be combined to recommend communities and test the effectiveness of the recommendations over a large real world dataset.