Abstract
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. Copyright 2013 ACM.