Recognizing intentions of E-commerce consumers based on ant colony optimization simulation

Abstract

Identification of a consumer’s intent has a vital impact on commodity recommendation, selection of hot drainage commodity, website layout, and link settings. Most of the present studies on user intent are considered static. Specific intent is accompanied by a specific environment. Thus, intent is static when the environment does not change. However, the uncertainty of user access and purchase in e-commerce activities indicates that user intent can assume multiple forms and has multiple developmental stages. Therefore, this study draws support from the core ideas of an ant colony algorithm. Ants represent users, and pheromones represent user intent. User intents of browsing, collection, cart shopping, and purchasing behavior are obtained from ant responses to pheromones. Pheromone is expressed as the inner product of the objective attribute of commodity and user perception ability, because user intent is the matching result of objective attributes of commodity and subjective feelings of users, and its value is the concentration of user intent pheromone. Thus, the dynamics and uncertainty of user intention development can be presented by the ant colony algorithm. In this study, data were obtained from a NetLogo simulation experiment. We used neural networks to identify and verify user intentions of browsing, collection, cart shopping, and purchasing. The experimental results showed that the accuracy of intention prediction increased from 48% to 67%, and a level of the 11–20% accuracy improvement shows good, realistic predictions.