A dynamic cold-start recommendation method based on incremental graph pattern matching. (2019)

• Record Type: Journal Article
• Authors: Zhang, Yanan; Yin, Guisheng; Chen, Deyun
• Is Part Of: International journal of computational science and engineering. Volume 18:Number 1(2019)
• Page Start: 89
• Page End: 100
• Publication Date: 2019
• Subjects: dynamic cold-start recommendation -- social network -- incremental graph pattern matching -- IGPM -- topology of social network; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

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.