A Three-Layered Mutually Reinforced Model for Personalized Citation Recommendation

Xiaoyan Cai, Junwei Han, Wenjie Li, Renxian Zhang, Shirui Pan, Libin Yang

科研成果: 期刊稿件文章同行评审

63 引用 (Scopus)

摘要

Fast-growing scientific papers pose the problem of rapidly and accurately finding a list of reference papers for a given manuscript. Citation recommendation is an indispensable technique to overcome this obstacle. In this paper, we propose a citation recommendation approach via mutual reinforcement on a three-layered graph, in which each paper, author or venue is represented as a vertex in the paper layer, author layer, and venue layer, respectively. For personalized recommendation, we initiate the random walk separately for each query researcher. However, this has a high computational complexity due to the large graph size. To solve this problem, we apply a three-layered interactive clustering approach to cluster related vertices in the graph. Personalized citation recommendations are then made on the subgraph, generated by the clusters associated with each researcher's needs. When evaluated on the ACL anthology network, DBLP, and CiteSeer ML data sets, the performance of our proposed model-based citation recommendation approach is comparable with that of other state-of-the-art citation recommendation approaches. The results also demonstrate that the personalized recommendation approach is more effective than the nonpersonalized recommendation approach.

源语言英语
文章编号8337085
页(从-至)6026-6037
页数12
期刊IEEE Transactions on Neural Networks and Learning Systems
29
12
DOI
出版状态已出版 - 12月 2018

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