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Abstract:
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We propose that in some product categories, individual consumer preferences may consist of multiple distinct tastes, each taste constituting of distinct attribute preferences. Such multi-taste preferences are likely present in categories characterized by large product attribute spaces and many diverse products, such as music, videos, restaurants, or books. Capturing heterogeneity among multi-taste consumers requires new methods, as consumers may share some tastes but not others - a different type of heterogeneity than captured by existing models. In this paper, we propose a model that allows for heterogeneity among consumers with multi-taste preferences, and an estimation procedure that scales to high dimensional attribute spaces. Through extensive simulation experiments, we demonstrate the proposed algorithm accurately recovers parameters, whereas single-taste benchmark models underfit and generate a misleading representation of both population and individual level preferences. We then apply the algorithm to a large data set of recipe choices to uncover rich patterns of preference heterogeneity. The proposed model fits the data better than single-taste benchmarks.
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