A preference elicitation interface for collecting dense recommender datasets with rich user information
This addresses the problem of sparse data for recommender systems researchers and social scientists, though it appears incremental as it builds on existing preference elicitation methods.
The authors developed an interface to efficiently collect dense preference data for visual stimuli along with user side-information, aiming to complement sparse industry datasets and enable research on human preference diversity, psychological effects, and counterfactual learning in recommender systems.
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.