Currently, readers often prefer to search for their interested papers based on a set of typed query keywords. As the keywords of a paper is often limited, paper recommender systems often need to recommend a set of papers which collectively satisfy the readers’ keyword query. However, the topics of recommended papers are probably not correlated with each other, which fail to meet the readers’ requirements on in-depth and continuous academic research. Furthermore, although existing paper citation graphs can model the papers’ correlations, they often face the data sparse problem which blocks accurate paper recommendations. To address these issues, we propose a keywords-driven and weight-aware paper recommendation approach, named LP-PRk+w (link prediction-paper recommendation), based on a weighted paper correlation graph. Concretely, we firstly optimize the existing paper citation graph modes by introducing a weighted similarity, after which we obtain a weighted paper correlation graph. Then we recommend a set of correlated papers based on the weighted paper correlation graph and the query keywords from readers. At last, we conduct large-scale experiments on a real-world Hep-Th dataset. Experimental results demonstrate that our proposal can improve the paper recommendation performances considerably, compared to other related solutions.