A General Consumer Preference Model for Experience Products: Application to Internet Recommendation Services

Abstract

The authors present a general consumer preference model for experience products that overcomes the limitations of consumer choice models, especially when it is not easy to consider some qualitative attributes of a product or when there are too many attributes relative to the available amount of preference data, by capturing the effects of unobserved product attributes with the residuals of reference consumers for the same product. They decompose the deterministic component of product utility into two parts: that accounted for by observed attributes and that due to nonobserved attributes. The authors estimate the unobserved component by relating it to the corresponding residuals of virtual experts representing homogeneous groups of people who experienced the product earlier and evaluated it. Their methodology involves identifying such virtual experts and determining the relative importance they should be given in the estimation of the target person's residuals. Using Bayesian estimation methods and Markov chain Monte Carlo simulation inference, the authors apply their approach to two types of consumer preference data: (1) online consumer ratings (stated preferences) data for Internet recommendation services and (2) offline consumer viewership (revealed preferences) data for movies. The results empirically show that this new approach outperforms several alternative collaborative filtering and attribute-based preference models with both in- and out-of-sample fits. The model is applicable to both Internet recommendation services and consumer choice studies.