Abstract
Authors and publishers use different metrics at various levels to estimate the impact of produced research, including the journal-level impact factor, the number of citations at an article-level and the H-index at an author-level. In this paper, we propose an approach to measure the Article Citation Impact (ACI) that will enable idenGEAtification of the impact of articles at their extended nearby citation network. We combine an article's content with its bibliometrics to evaluate the citation impact of articles in their surrounding citation network. Using the article metadata, we calculate the semantic similarity between two articles in the extended network. The articles' similarity and bibliometric scores are then used to assess the impact of the article among their extended nearby citation network. In our empirical studies, we use two datasets to validate the efficiency of our approach to evaluate the impact of articles on improving article recommendation processes. The experimental results highlight the effectiveness of the proposed approach to optimize the overall recommendation quality, compared to other baseline approaches.