An Incremental Graph Pattern Matching Based Dynamic Cold-Start Recommendation Method

In order to give accurate recommendations for cold-start user, researchers use social network to find similar users. These efforts assume that cold-start user’s social relationships are static. However social relationships of cold-start user may change as time pass by. In order to give accurate and timely in manner recommendations for cold-start user, it is need to update social relationship continuously. In this paper, we proposed an incremental graph pattern matching based dynamic cold-start recommendation method (IGPMDCR), which updates similar users for cold-start user based on topology of social network, and gives recommendations based on the latest similar users’ records. The experimental results show that, IGPMDCR could give accurate and timely in manner recommendations for cold-start user.

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