In this paper, we propose POLAR, an attention-based CNN combined with one-shot learning for personalized article recommendation. Given a query, POLAR uses an attention-based CNN to estimate the relevance score between the query and related articles. The attention mechanism can help significantly improve the relevance estimation. For example, on AMiner, this can help achieve a +5.0% improvement in terms of NDCG@3. One more challenge in personalized article recommendation is how to collect statistically sufficient training data for a recommendation model. POLAR combines a one-shot learning function into the recommendation model, which further gains significant improvements. For example, on AMiner, with only 1.6 feedbacks on average, POLAR achieves 2.7% improvement by NDCG@3. We evaluate the proposed POLAR on three different datasets: AMiner, Patent, and RARD. Experimental results demonstrate the effectiveness of the proposed model. Recently, we have successfully deployed POLAR into AMiner as the recommendation engine for article recommendation, which further confirms the effectiveness of the proposed model.