We present a crowdsourcing (CS) study to examine how specific attributes probabilistically affect the selection and sequencing of images from personal photo collections. Thirteen image attributes are explored, including seven people-centric properties. We first propose a novel dataset shaping technique based on mixed integer linear programming (MILP) to identify a subset of photos in which the attributes of interest are uniformly distributed and minimally correlated. Shaping enables the synthesis of compact, balanced, and representative datasets for CS, and facilitates effective learning of the selection likelihood of an image as well as its relative position in a sequence, given its attributes. We further present an ILP-based slideshow creation framework to select and arrange (a subset of) appealing images from a personal photo library. Quantitative and qualitative evaluations confirm that our method outperforms regression-based and greedy approaches for photo selection and sequencing, generating slideshows similar in quality to those created by humans.