Cross-domain citation recommendation based on hybrid topic model and co-citation selection Online publication date: Fri, 08-Sep-2017
by Supaporn Tantanasiriwong; Sumanta Guha; Paul Janecek; Choochart Haruechaiyasak; Leif Azzopardi
International Journal of Data Mining, Modelling and Management (IJDMMM), Vol. 9, No. 3, 2017
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.
Online publication date: Fri, 08-Sep-2017
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