The community question and answering (CQA) sites such as Stack Overflow are used by many users around the world to obtain answers to technical questions. Here, the reliability of a user is determined using metrics such as reputation score. It is important for the CQA sites to assess the reputation score of the new users joining the site. Accurate estimation of reputation scores of these cold start users can help in tasks like question routing, expert recommendation and ranking etc. However, lack of activity information makes it quite difficult to assess the reputation score for new users. We propose an approach which makes use of alternate data associated with the users to predict the reputation score of the new users. We show that the alternate data obtained using users’ personal websites could improve the reputation score performance. We develop deep learning models based on feature distillation, such as the student-teacher models, to improve the reputation score prediction of new users from the alternate data. We demonstrate the effectiveness of the proposed approaches on the publicly available stack overflow data and publicly available alternate data. Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.