Citation Recommendation based on Citation links Explanation

Abstract

With the rapid increase of scientific papers, it is difficult for researchers to obtain appropriate references. Due to the black-box property of neural network, the existing content-based citation recommendation methods are mainly embedding text into a low-dimensional space, and citation links are not interpretable (e.g., paper A cites paper B because of topic X). To solve this problem, we propose a Weighted Citation Network with Citation Links Explanation (WCN-CLE) algorithm. Because citation links mainly depends on the content, we need to explore textual reasons for citation links. Firstly, we extract word fragments through citation network information for each citation link as its explanation. Secondly, we construct a weighted HIN network with two kinds of vertex (paper and author). The weight of each edge is calculated by its word fragments. Then the query document is linked to the HIN network through author information and word fragments similarity with candidate papers. Finally, each vertex's feature representation is obtained by network representation learning method, and recommend papers through vector similarity. Experimental results on two real-world datasets show that WCN-CLE has better performance due to its integration of structural information and semantic information.