Recommending Products When Consumers Learn Their Preferences

Consumers often learn their preferences as they search. For example, after test driving new cars, a consumer might find she undervalued trunk space and overvalued sunroofs. Preference learning makes search complex because, each time a product is searched, updated preferences affect the value of all products and the value of subsequent (optimal) search. Recommendations that take preference learning into account help consumers navigate search. We motivate a model in which consumers learn (update) the preferences they ascribe to attribute levels as they search. Formal results suggest modifications to the common foci in the search literature and the recommendation-system literature. It may not be optimal to recommend the product with the highest option value, as in most search models, or the product that is most likely to be chosen or has the highest expected utility, as in traditional recommendation systems. Recommendations are more effective if they encourage consumers to search undervalued products and/or products with diverse attributes. Both modifications enhance the value of preference learning. Recommendation systems, based on the formal theory, outperform benchmark models in synthetic worlds, especially when consumers are novices and when the recommendation system can develop its own priors by targeting relatively homogeneous segments.