Dynamic preference elicitation of customer behaviours in e-commerce from online reviews based on expectation confirmation theory

Preference change, also known as preference drift, is one of the factors that online retailers need to consider to accurately collect consumer preferences and make personalised recommendations. Online reviews have been widely used to analyse the preference drift of consumers. However, previous studies on online reviews ignored the psychological perceptions of consumers in terms of satisfaction. This paper aims to develop a method for dynamic preference elicitation from online reviews based on exploring the theory of consumer satisfaction formation. Based on the framework of expectation confirmation theory, we develop formulas for expressing the relations among expectation, perceived performance, confirmation, and satisfaction. We then use the proposed dynamic preference elicitation model to predict the change of consumer overall preference after each review and rank products for consumers' next purchase. We test the proposed approach with a case study based on a data set from Amazon.com. It is founded that the satisfaction changes in each purchase, and this change will affect the prediction of the next product ranking. The case study is based on one product group, and further research is needed to see if the operation of the proposed method can be extended to other kinds of products.