Social Information Seeking (SIS) is a platform for easy and informal interaction among participants. This has been popularized by many of the major prevailing Community Question Answering (CQA) forums, such as Quora, Stackoverflow, etc. A user submits a question to the forum that is later answered by one or more of the users in the forum. These sites facilitate knowledge exchange between users through questions and answers. A large pool of knowledge has since been created. Although hundreds of questions are posted every hour, many of them remain unanswered due to lower quality, wrong choice of question tags, bad routing of questions to experts, and dearth of proper answerers. In this paper, we propose a solution to the problem of routing questions to appropriate experts by utilizing the tag and time information of the posted questions. We propose a recommendation model that uses a tag network and temporal analysis to find the potential experts. Our experiments over a dataset from Stack Exchange show the effectiveness of our approach over several baseline models. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.