  Different from shopping at retail stores, consumers on e-commerce platforms
usually cannot touch or try products before purchasing, which means that they
have to make decisions when they are uncertain about the outcome (e.g.,
satisfaction level) of purchasing a product. To study people's preferences,
economics researchers have proposed the hypothesis of Expected Utility (EU)
that models the subject value associated with an individual's choice as the
statistical expectations of that individual's valuations of the outcomes of
this choice. Despite its success in studies of game theory and decision theory,
the effectiveness of EU, however, is mostly unknown in e-commerce
recommendation systems. Previous research on e-commerce recommendation
interprets the utility of purchase decisions either as a function of the
consumed quantity of the product or as the gain of sellers/buyers in the
monetary sense. As most consumers just purchase one unit of a product at a time
and most alternatives have similar prices, such modeling of purchase utility is
likely to be inaccurate in practice. In this paper, we interpret purchase
utility as the satisfaction level a consumer gets from a product and propose a
recommendation framework using EU to model consumers' behavioral patterns. We
assume that consumer estimates the expected utilities of all the alternatives
and choose products with maximum expected utility for each purchase. To deal
with the potential psychological biases of each consumer, we introduce the
usage of Probability Weight Function (PWF) and design our algorithm based on
Weighted Expected Utility (WEU). Empirical study on real-world e-commerce
datasets shows that our proposed ranking-based recommendation framework
achieves statistically significant improvement against both classical
Collaborative Filtering/Latent Factor Models and state-of-the-art deep models
in top-K recommendation.
