Title: A dynamic cold-start recommendation method based on incremental graph pattern matching

Authors: Yanan Zhang; Guisheng Yin; Deyun Chen

Addresses: Post-doctoral Research Station of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China; Software School, Harbin University of Science and Technology, Harbin, 150040, China ' College of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, China ' School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China

Abstract: In order to give accurate recommendations for cold-start user who has few records, researchers find similar users for cold-start user according to social network. However these efforts assume that cold-start user's social relationships are static and ignore updating social relationships are time consuming. In social network, cold-start user and other users may change their social relationships as time passes. In order to give accurate and timely recommendations for cold-start user, it is necessary to update similar users for cold-start users according to their latest social relationship continuously. In this paper, an incremental graph pattern matching based dynamic cold-start recommendation method (IGPMDCR) is proposed, which updates similar users for cold-start user based on topology of social network, and gives recommendations according to latest users similar to cold-start user. The experimental results show that IGPMDCR could give accurate and timely recommendations for cold-start user.

Keywords: dynamic cold-start recommendation; social network; incremental graph pattern matching; IGPM; topology of social network.

DOI: 10.1504/IJCSE.2019.096948

International Journal of Computational Science and Engineering, 2019 Vol.18 No.1, pp.89 - 100

Received: 22 Jun 2016
Accepted: 30 Aug 2016
Published online: 14 Dec 2018 *

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