We formalize sequential decision–making with information acquisition as the Probing-augmented User-Centric Selection (PUCS) framework, where a learner first probes a subset of arms to obtain side information on resources and rewards, and then assigns K plays to M arms. PUCS encompasses practical scenarios such as ridesharing, wireless scheduling, and content recommendation, in which both resources and payoffs are initially unknown and probing incurs cost. For the offline setting (known payoff distributions), we present a greedy probing algorithm with a constant-factor approximation guarantee of ζ=(e-1)/(2e-1). For the online setting (unknown payoff distributions), we introduce OLPA, a stochastic combinatorial bandit algorithm that achieves a regret bound of O(√T+ln²T). We also prove an Ω(√T) lower bound, showing that the upper bound is tight up to logarithmic factors. Numerical results using two real-world datasets demonstrate the effectiveness of our solutions.