【摘 要】
：
Detecting the evolution properties of online user preference is of significance for deeply understanding online collective behaivors.In this talk,we will pr
【机 构】
：
Univ.of Shanghai for Sci.& Tech.
论文部分内容阅读
Detecting the evolution properties of online user preference is of significance for deeply understanding online collective behaivors.In this talk,we will present advanced progresses of our group for collective behavior analysis,including the local memory effect and evolution dynamics of online user preference,generated by the online rating and selecting behaviors,the correlation between the user taste and the trust formation,the node importance identification method and so on.
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