  With the increasingly fierce market competition, offering a free trial has
become a potent stimuli strategy to promote products and attract users. By
providing users with opportunities to experience goods without charge, a free
trial makes adopters know more about products and thus encourages their
willingness to buy. However, as the critical point in the promotion process,
finding the proper adopters is rarely explored. Empirically winnowing users by
their static demographic attributes is feasible but less effective, neglecting
their personalized preferences. To dynamically match the products with the best
adopters, in this work, we propose a novel free trial user selection model
named SMILE, which is based on reinforcement learning (RL) where an agent
actively selects specific adopters aiming to maximize the profit after free
trials. Specifically, we design a tree structure to reformulate the action
space, which allows us to select adopters from massive user space efficiently.
The experimental analysis on three datasets demonstrates the proposed model's
superiority and elucidates why reinforcement learning and tree structure can
improve performance. Our study demonstrates technical feasibility for
constructing a more robust and intelligent user selection model and guides for
investigating more marketing promotion strategies.
