  Designing recommendation systems with limited or no available training data
remains a challenge. To that end, a new combinatorial optimization problem is
formulated to generate optimized item selection for experimentation with the
goal to shorten the time for collecting randomized training data. We first
present an overview of the optimized item selection problem and a multi-level
optimization framework to solve it. The approach integrates techniques from
discrete optimization, unsupervised clustering, and latent text embeddings. We
then discuss how to incorporate optimized item selection with active learning
as part of randomized exploration in an ongoing fashion.
