A Three-Layered Mutually Reinforced Model for Personalized Citation Recommendation

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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

56 Citations (Scopus)


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.

Original languageEnglish
Article number8337085
Pages (from-to)6026-6037
Number of pages12
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number12
Publication statusPublished - 1 Dec 2018


  • Mutually reinforced model
  • personalized citation recommendation
  • three-layered interactive clustering

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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