  In recent years, recommendation systems have been widely applied in many
domains. These systems are impotent in affecting users to choose the behavior
that the system expects. Meanwhile, providing incentives has been proven to be
a more proactive way to affect users' behaviors. Due to the budget limitation,
the number of users who can be incentivized is restricted. In this light, we
intend to utilize social influence existing among users to enhance the effect
of incentivization. Through incentivizing influential users directly, their
followers in the social network are possibly incentivized indirectly. However,
in many real-world scenarios, the topological structure of the network is
usually unknown, which makes identifying influential users difficult. To tackle
the aforementioned challenges, in this paper, we propose a novel algorithm for
exploring influential users in unknown networks, which can estimate the
influential relationships among users based on their historical behaviors and
without knowing the topology of the network. Meanwhile, we design an adaptive
incentive allocation approach that determines incentive values based on users'
preferences and their influence ability. We evaluate the performance of the
proposed approaches by conducting experiments on both synthetic and real-world
datasets. The experimental results demonstrate the effectiveness of the
proposed approaches.
