User-Involved Preference Elicitation for Product Search and Recommender Systems

products or services, planning a trip, or scheduling resources, people increasingly rely on computerized product recommender systems (also called product search tools) to find outcomes that best satisfy their needs and preferences. However, automated decision systems cannot effectively search the space of possible solutions without an accurate model of a user’s preferences. Preference acquisition is therefore a fundamental problem of growing importance. Without an adequate interaction model and system guidance, it is difficult for users to establish a complete and accurate model of their preferences. More specifically, we face the following difficulties: First, inadequate elicitation tools can easily mislead users to focus on means objectives rather than fundamental decision objectives and force them to state preferences in the wrong order. For example, a user who commits to the choice of minivans (means objective) for spacious baggage space (fundamental) is not focusing on the values and could risk missing alternatives offered by station wagons. In value-focus thinking, Keeney (1992) suggests that the specification and clarification of values should not be overtaken by the set of alternatives too rapidly. This theory has a direct implication on the order in which the system initially elicits user preferences. Second, users are not aware of all preferences until they see them violated. For example, a user does not think of stating a preference for the intermediate airport until a solution proposes an airplane change in a place the user dislikes. This observation sheds light on the interaction design guideline on how to help users discover their hidden preferences. Finally, preferences can be inconsistent. Users can state preference valArticles