Citizen science projects rely on volunteers to contribute time and effort to solve scientific problems, but the majority of citizen science users typically contribute to only one or two projects. Recommender systems have been recently used in citizen science to motivate people to contribute to additional projects. However, these systems often recommend the more popular projects at the expense of less popular projects which need people's contributions more critically. In this work we develop a post processing approach for enhancing "long-tail"item recommendation that can be applied to any recommendation system. We propose a novel re-ranking model, based on the lift measure used in machine learning, which considers item co-occurrence as well as popularity for enhancing long tail recommendations. We demonstrate the efficacy of our approach in the citizen science domain on two data sets and three state of the art recommendation algorithms, comparing hit rate before and after applying lift boosting. Additionally, we compare our approach to two predetermined re-ranking baselines. Results show that our proposed approach significantly improves performance for tail item recommendation without a substantial loss in head item and overall item recommendation performance. Our approach is general and can be naturally applied to existing recommendation systems in citizen science that personalize project suggestions to users, potentially leading to an increase in efficiency and performance.