Evolutive multi-attribute decision making with online consumer reviews

In the digital age, the sheer volume of online consumer reviews imposes a cognitive burden on consumers, complicating their purchasing decisions. Many studies have integrated consumer opinions to provide consumers with clear and concise information. However, these studies often prioritize mainstream opinions, overlooking the diversity and timeliness of other important perspectives. To address this challenge, we propose an evolutive decision-making method. Firstly, we propose an attribute rating evolution algorithm to address the online reviews based on the iterative self-organizing data analysis technique and time decay. This algorithm enables real-time analysis of the diverse opinions expressed in review data. Then, taking into account consumer attribute preferences and decision-making psychology, we formulate multiple product ranking strategies to offer personalized decisions based on the evolutive opinions. Our method decreases the bias towards review quantity, ensuring that significant opinions are not overshadowed by more frequent ones. Through data experiments and an application on OpenTable.com, we demonstrate that our method can provides effective decision recommendation for consumers.