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In recent years, recommendation systems have been widely applied in manydomains. These systems are impotent in affecting users to choose the behaviorthat the system expects. Meanwhile, providing incentives has been proven to bea 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, weintend to utilize social influence existing among users to enhance the effectof incentivization. Through incentivizing influential users directly, theirfollowers in the social network are possibly incentivized indirectly. However,in many real-world scenarios, the topological structure of the network isusually unknown, which makes identifying influential users difficult. To tacklethe aforementioned challenges, in this paper, we propose a novel algorithm forexploring influential users in unknown networks, which can estimate theinfluential relationships among users based on their historical behaviors andwithout knowing the topology of the network. Meanwhile, we design an adaptiveincentive allocation approach that determines incentive values based on users'preferences and their influence ability. We evaluate the performance of theproposed approaches by conducting experiments on both synthetic and real-worlddatasets. The experimental results demonstrate the effectiveness of theproposed approaches.

Identifying Influential Users in Unknown Social Networks for Adaptive Incentive Allocation Under Budget Restriction