Estimating and predicting user subjective perceptions on software products is a challenging, yet increasingly important, endeavour. As an extreme case study, we consider the problem of exploring computer-generated art object combinations that will please the maximum number of people. Since it is not feasible to gather feedbacks for all art products because of a combinatorial explosion of possible configurations as well as resource and time limitations, the challenging objective is to rank and identify optimal art product variants that can be generated based on their average likability. We present the use of Software Product Line (SPL) techniques for gathering and leveraging user feedbacks within the boundaries of a variability model. Our approach is developed in two phases: 1) the creation of a data set using a genetic algorithm and real feedback and 2) the application of a data mining technique on this data set to create a ranking enriched with confidence metrics. We perform a case study of a real-world computer-generated art system. The results of our approach on the arts domain reveal interesting directions for the analysis of user-specific qualities of SPLs.