  Consider a movie studio aiming to produce a set of new movies for summer
release: What types of movies it should produce? Who would the movies appeal
to? How many movies should it make? Similar issues are encountered by a variety
of organizations, e.g., mobile-phone manufacturers and online magazines, who
have to create new (non-existent) items to satisfy groups of users with
different preferences. In this paper, we present a joint problem formalization
of these interrelated issues, and propose generative methods that address these
questions simultaneously. Specifically, we leverage the latent space obtained
by training a deep generative model---the Variational Autoencoder (VAE)---via a
loss function that incorporates both rating performance and item reconstruction
terms. We then apply a greedy search algorithm that utilizes this learned
latent space to jointly obtain K plausible new items, and user groups that
would find the items appealing. An evaluation of our methods on a synthetic
dataset indicates that our approach is able to generate novel items similar to
highly-desirable unobserved items. As case studies on real-world data, we
applied our method on the MART abstract art and Movielens Tag Genome dataset,
which resulted in promising results: small and diverse sets of novel items.
