Citation recommendation based on citation tendency

• Published in: Scientometrics Vol. 121; no. 2; pp. 937 - 956
• Main Authors: Chen, Xi, Zhao, Huan-jing, Zhao, Shu, Chen, Jie, Zhang, Yan-ping
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.11.2019; Springer Nature B.V
• Subjects: Algorithms; Citation analysis; Computer Science; Embedding; Fuses; Information Storage and Retrieval; Library Science; Nodes; Random walk; Citation recommendation; 68T99; C63; Network representation; C89; Heterogeneous information network; Citation tendency
• ISSN: 0138-9130; 1588-2861
• DOI: 10.1007/s11192-019-03225-6

Due to the development of academic, more and more attentions are paid to citation recommendation. To solve the citation recommendation problem, researchers begin to focus on the network representation, because it fuses semantic information and structural information well. It is a big challenge that how to map articles in a heterogeneous information network into a low-dimensional space while preserving the potential associations between articles. We propose a novel citation recommendation algorithm based on citation tendency, named CIRec which learns more about the potential relationship of articles in the process of network embedding. Citation tendency means if an article can be selected as a reference, it probability satisfies some kinds of conditions. In our algorithm, five weight matrices which represent the probability of entity-to-entity migration based on citation tendency are defined to build weighted heterogeneous network first. Second, we design a biased random walk procedure which efficiently explores articles’ characteristics and citations information. Finally, the skip-gram model is used to learn the neighborhood relationship of the nodes in the walk sequence and map the nodes to the vector space. Comparing with existing state-of-the-art technique, experiment results show that our algorithm CIRec has better recall, precision, NDCG on AAN and DBLP dataset.