A Dynamic Cold-Start Recommendation Method Based On Incremental Graph Pattern Matching

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.