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

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 Author 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.