There has been a massive rise in the use of Community Question and Answering (CQA) forums to get solutions to various technical and non-technical queries. One common problem faced in CQA is the small number of experts, which leaves many questions unanswered. This paper addresses the challenging problem of predicting the best answerer for a new question and thereby recommending the best expert for the same. Although there are work in the literature that aim to find possible answerers for questions posted in CQA, very few algorithms exist for finding the best answerer whose answer will satisfy the information need of the original Poster. For finding answerers, existing approaches mostly use features based on content and tags associated with the questions. There are few approaches that additionally consider the users' history. In this paper, we propose an approach that considers a comprehensive set of features including but not limited to text representation, tag based similarity as well as multiple user-based features that target users' availability, agility as well as expertise for predicting the best answerer for a given question. We also include features that give incentives to users who answer less but more important questions over those who answer a lot of questions of less importance. A learning to rank algorithm is used to find the weight of each feature. Experiments conducted on a real dataset from Stack Exchange show the efficacy of the proposed method in terms of multiple evaluation metrics for accuracy, robustness and real time performance. © 2018 Association for Computing Machinery.