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We present an interface that can be leveraged to quickly and effortlesslyelicit people's preferences for visual stimuli, such as photographs, visual artand screensavers, along with rich side-information about its users. We plan toemploy the new interface to collect dense recommender datasets that willcomplement existing sparse industry-scale datasets. The new interface and thecollected datasets are intended to foster integration of research inrecommender systems with research in social and behavioral sciences. Forinstance, we will use the datasets to assess the diversity of human preferencesin different domains of visual experience. Further, using the datasets we willbe able to measure crucial psychological effects, such as preferenceconsistency, scale acuity and anchoring biases. Last, we the datasets willfacilitate evaluation in counterfactual learning experiments.

A preference elicitation interface for collecting dense recommender datasets with rich user information