Saleh, AhmedMai, FlorianNishioka, ChifumiScherp, AnsgarEibl, MaximilianGaedke, Martin2017-08-282017-08-282017978-3-88579-669-5An enormous volume of scientific content is published every year. The amount exceeds by far what a scientist can read in her entire life. In order to address this problem, we have developed and empirically evaluated a recommender system for scientific papers based on Twitter postings. In this paper, we improve on the previous work by a reranking approach using Deep Learning. Thus, after a list of top-k recommendations is computed, we rerank the results by employing a neural network to improve the results of the existing recommender system. We present the design of the deep reranking approach and a preliminary evaluation. Our results show that in most cases, the recommendations can be improved using our Deep Learning reranking approach.enrecommender systemsdeep learningsemantic profiling10.18420/in2017_2161617-5468