Generating Transparent, Steerable Recommendations from Textual Descriptions of Items

We propose a recommendation technique that works by collecting text descriptions of the items that we want to recommend and then using this emph{textual aura} to compute the similarity between items using techniques drawn from information retrieval. We show how this representation can be used to explain the similarities between items using terms from the textual aura and further how it can be used to steer the recommender. We'll describe a system that demonstrates these techniques and we'll detail some preliminary experiments aimed at evaluating the quality of the recommendations and the effectiveness of the explanations of item similarity.