Recommendations from a system to a person require thoughtful decisions. People prefer quick, relevant responses, ones that quickly lead to an acceptable choice. A system’s recommendations, however, often rely on pairwise similarity between items or between users, based on a distance metric. This paper advocates a broader, set-based similarity based on text descriptions of known items, and on the items users have found appealing in the past. The text, autonomously gathered from the Web, proves to be a strong indicator of similarities among sets of items, as well as a source of useful diversity. Empirical results provide insight into the complexities of similarity, of user behavior, and of cognition in the system that formulates recommendations.