  We present an interface that can be leveraged to quickly and effortlessly
elicit people's preferences for visual stimuli, such as photographs, visual art
and screensavers, along with rich side-information about its users. We plan to
employ the new interface to collect dense recommender datasets that will
complement existing sparse industry-scale datasets. The new interface and the
collected datasets are intended to foster integration of research in
recommender systems with research in social and behavioral sciences. For
instance, we will use the datasets to assess the diversity of human preferences
in different domains of visual experience. Further, using the datasets we will
be able to measure crucial psychological effects, such as preference
consistency, scale acuity and anchoring biases. Last, we the datasets will
facilitate evaluation in counterfactual learning experiments.
