Predicating paper influence in academic network
Yi Xie, Yuqing Sun, Lei Shen · 2016
It is meaningful to recommend appropriate works to a researcher. One important consideration is the relatedness to one's interests. Although it can be expressed by one's query on an academic dataset, there often exists some semantic ambiguity in relatedness computation that are caused by personalized vocabularies of authors and queriers. Another considered aspect is the quality of a publication, which is often justified by the number and quality of its citations. But it is difficult to estimate the potential influence of a new publication when it has few citation. In this paper, we try to solve the two problems in academic recommendation. To reduce the semantic ambiguity, domain knowledge is created by learning the inherit relativity of word usage from an academic dataset. To compute the potential influence of a new publication,we taking into account the contents and venue of a paper, as well as the reputation of its authors. A recommendation algorithm is designed to find the top k related and influential papers for a query from new publications. We verify the proposed method on real dataset.