Cross-domain citation recommendation based on hybrid topic model and co-citation selection citation selection

Supaporn Tantanasiriwong, Sumanta Guha, Paul Janecek, Choochart Haruechaiyasak, Leif Azzopardi

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Abstract

Cross-domain recommendations are of growing importance in the research community. An application of particular interest is to recommend a set of relevant research papers as citations for a given patent. This paper proposes an approach for cross-domain citation recommendation based on the hybrid topic model and co-citation selection. Using the topic model, relevant terms from documents could be clustered into the same topics. In addition, the co-citation selection technique will help select citations based on a set of highly similar patents. To evaluate the performance, we compared our proposed approach with the traditional baseline approaches using a corpus of patents collected for different technological fields of biotechnology, environmental technology, medical technology and nanotechnology. Experimental results show our cross domain citation recommendation yields a higher performance in predicting relevant publication citations than all baseline approaches.

Original languageEnglish
Pages (from-to)220-236
Number of pages17
JournalInternational Journal of Data Mining, Modelling and Management
Volume9
Issue number3
Early online date13 Sep 2017
DOIs
Publication statusE-pub ahead of print - 13 Sep 2017

Keywords

  • analysis of variance
  • Anova
  • ccs
  • cdcr
  • co-citation selection
  • cross domain citation recommendation
  • cross domain recommender system
  • evaluation
  • information retrieval
  • keyphrase extraction tool
  • similarity measures
  • topic model

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