Title: Patent personalised recommendation method based on fusing co-occurrence network and point mutual information

Authors: Deng Na; Liu Chang

Addresses: School of Computer Science, Hubei University of Technology, Wuhan, Hubei, China ' School of Computer Science, Hubei University of Technology, Wuhan, Hubei, China

Abstract: In the emerging high-tech industry, the number of patents is growing particularly rapidly. In this background, timely and accurate identification of patents that are closely related to enterprises and have significant influence for realising patent transformation and promoting enterprise development. In this paper, we propose a recommendation method based on co-occurrence network and Point Mutual Information coefficient (PMI). Through experiments on the patent texts in the communication industry, this paper finds that the patents recommended are highly compatible with the development direction of the enterprise, which can provide high value for the development of the enterprise. It verifies that the method of this paper is valid in the field of patent recommendation, and provides new ideas for improving the conversion rate of patents and promoting the application of scientific and technological achievements.

Keywords: patent personalised recommendation; co-occurrence network; point mutual information coefficient; text clustering.

DOI: 10.1504/IJGUC.2024.140979

International Journal of Grid and Utility Computing, 2024 Vol.15 No.5, pp.466 - 483

Received: 28 Nov 2023
Accepted: 02 Mar 2024
Published online: 05 Sep 2024 *

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