Citation Recommendation via Proximity Full-Text Citation Analysis and Supervised Topical Prior

نویسندگان

  • Xiaozhong Liu
  • Jinsong Zhang
  • Chun Guo
چکیده

Currently the many publications are now available electronically and online, which has had a significant effect, while brought several challenges. With the objective to enhance citation recommendation based on innovative text and graph mining algorithms along with full-text citation analysis, we utilized proximitybased citation contexts extracted from a large number of full-text publications, and then used a publication/citation topic distribution to generate a novel citation graph to calculate the publication topical importance. The importance score can be utilized as a new means to enhance the recommendation performance. Experiment with full-text citation data showed that the novel method could significantly (p < 0.001) enhance citation recommendation performance.

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تاریخ انتشار 2016