We define preference elicitation as an interaction, consisting of a sequence of computer queries and human implicit feedback (binary choices), from which the user’s most preferred design can be elicited. The difficulty of this problem is that, while a human-computer interaction must be short to be effective, query algorithms usually require lengthy interactions to perform well. We address this problem in two steps. A black-box optimization approach is introduced: The query algorithm retrieves and updates a user preference model during the interaction and creates the next query containing designs that are both likely to be preferred and different from existing ones. Next, a heuristic based on accumulated elicitations from previous users is employed to shorten the current elicitation by querying preferred designs from previous users (the “crowd”) who share similar preferences to the current one.

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