Algorithm 1: The UWUSRank Algorithm

Input: Dynamic User Interaction Network $G_T$ Output: UWUSRank for each user //Calculate the user’s own influence for all  $u$ in $U$do Compute $act\left(u\right)$ using Equation (5) Compute $res\left(u\right)$ using Equation (6) Compute $aut\left(u\right)$ using Equation (7) $I_0\left(u\right)={\varphi }_1\cdot act\left(u\right)+{\varphi }_2\cdot res\left(u\right)+{\varphi }_3\cdot aut\left(u\right)$ Initialize the influence value of each user as $I_0\left(u\right)$ end for //Calculate user influence considering the user’s own influence and Interaction behavior for $iter=1$ to $max\_iter$ do        while $\left(u,v\right)\in$E        Compute ${\omega }_t\left(u,v\right)$ using Equation (9)        Compute ${\gamma }_t\left(u,v\right)$ using Equation (4)           $Affec{t}_t\left(u\right)=\left(1-d\right)\times I_0\left(u\right)+d\times \sum _{v\in O_{\left(u\right)}}(Affec{t}_t\left(v\right)\times {\omega }_t\left(u,v\right)\times {\gamma }_t\left(u,v\right))$            $\delta ={\displaystyle \sum _{i=1}^N}Affec{t}_{iter}\left(u\right)-Affec{t}_{iter-1}\left(u\right)$           if converged($\delta$) then               break           end if        end while for all $u$ in $U$do     //Calculate the similarity between user interest and the topic content                                             $sim\left(u,topic\right)=\frac{ver\left(u\right)\cdot ver\left(topic\right)}{ver(u)\times ver(topic)}$     //Calculate the UWUSRank value                                     $UWUSRan{k}_t\left(u\right)=Affec{t}_t\left(u\right)\cdot sim\left(u,topic\right)$ return $UWUSRan{k}_t$ for each user