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Nowadays, department stores and online merchants usually develop some price promotion strategies to attract customers and increase their purchase intention. Therefore, it is significant for customers to pick out attractive products and obtain the maximum discount rate. Admittedly, the skyline query is a most useful tool to find out attractive products. However, it does little to help select the product combinations with the maximum discount rate. Motivated by this, we identify an interesting problem, a most preferential skyline product (MPSP) combination discovering problem, which is NP-hard, for the first time in the literature. This problem aims to report all skyline product combinations having the maximum discount rate. Since the exact algorithm for the MPSP is not scalable to large or high-dimensional datasets, we design an approximate algorithm that guarantees the accuracy of the results. The experiment results demonstrate the efficiency and effectiveness of our proposed algorithms.