Abstract: Nowadays,with the rise of social media,various and disparate social media services spring up like mushrooms.As a result,the social media variety phenomenon has become more and more pervasive.In this paper,we proposed a novel association rule-based method to investigate into this social media variety phenomenon,which aims to mine the cross-network knowledge association by leveraging the collective intelligence of plenty of cross-network overlapped users.A cold-start video recommendation application was further designed based on the derived cross-network knowledge association.Three stages are mainly involved in the framework:(1)heterogeneous topic modeling,where YouTube videos and Twitter users are modeled in topic level；(2)association rule-based knowledge association,where overlapped users serve as bridge between different social media networks and a novel association rule-based method is used to derive the topic correlation between different networks；(3)cold-start video recommendation,where the Twitter users and YouTube videos are transferred to the same topic space and matched on topic level.The experiments on a real-world dataset demonstrate the effectiveness of the proposed association method,which is able to capture some more flexible knowledge association beyond the semantic association.Moreover,the performance of the cold-start video re-commendation application is also very promising.

Key words: Cross-network association,Association rules mining,Video recommendation