Hybrid recommender systems combine several different sources of information to generate recommendations. These systems demonstrate improved accuracy compared to single-source recommendation strategies. However, hybrid recommendation strategies are inherently more complex than those that use a single source of information, and thus the process of explaining recommendations to users becomes more challenging. In this paper we describe a hybrid recommender system built on a probabilistic programming language, and discuss the benefits and challenges of explaining its recommendations to users. We perform a mixed model statistical analysis of user preferences for explanations in this system. Through an online user survey, we evaluate explanations for hybrid algorithms in a variety of text and visual, graph-based formats, that are either novel designs or derived from existing hybrid recommender systems.
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