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

Abstract

We 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 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. For this purpose, we decompose the deterministic component of product utility into two parts: the observed component accounted for by observed attributes and the unobserved component due to non-observed attributes. The unobserved component is estimated by relating it to the corresponding residuals of virtual experts representing homogeneous groups of persons who had experienced the product earlier and evaluated it. Our methodology involves identifying such virtual experts and determining the relative importance to be given to them in the estimation of the target person’s residuals. Using Bayesian estimation methods and MCMC simulation inference, we applied our 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. We empirically show that our new approach outperforms several alternative collaborative filtering and attribute-based preference models with both in-sample and out of sample fits. Our model is applicable to both Internet recommendation services and consumer choice studies.

Keywords: Consumer Model for Stated/Revealed Preference Data, Recommendation Systems, Virtual Experts, Experience Products, Bayesian estimation, MCMC Simulation