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

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

International Journal of Data Mining, Modelling and Management, 2017, vol. 9, issue 3, 220-236

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.

Keywords: cross domain recommender system; citation recommendation; cross domain citation recommendation; CDCR; topic model; co-citation selection; CCS; information retrieval; keyphrase extraction tool; similarity measures; evaluation; analysis of variance; ANOVA. (search for similar items in EconPapers)
Date: 2017
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