Consumer preference estimation based on intertemporal choice data: A chance constrained data envelopment analysis method

Choice behavior reflects consumer preferences. Consumers often purchase products online nowadays, which can be viewed as a choice process. If a consumer makes multiple transactions over a period of time, then we can say the consumer make multiple intertemporal choices. This study focuses on the problem of learning consumer preferences from intertemporal choice data. The main challenges in this research include the ratio relationship between some attributes, the variability of choice set and the uncertainty of attribute values. To address these challenges, we propose a consumer preference model based on the chance-constrained data envelopment analysis (DEA) framework. In the model, we assume consumer choice has maximum utility value, and define a performance cost utility function to capture the ratio relationship between some attributes. We then develop two scenarios for the consumer preference model, depending on whether the uncertain variables are correlated. The estimated consumer preferences can be used to predict each consumer's choice and item ranking. To validate our model, we conduct two numerical experiments, and analyze the impact of some parameters on the preference and evaluation results. The results show that the estimated preference values are accurate when the values of risk indicator and correlation coefficients are small, and our model performs well on the predictions of choice and item ranking.