E - Commerce Consumer Intention Recognition Based On Perceptual Pheromone Ant Colony Algorithm

With the popularity of the Internet business applications, the demand of consumer experience become the main driving factor in the development of the Internet. At the same time, the social network of electronic commerce for the emergence of new changes. Electronic commerce system is providing more and more choices for users, and its structure becomes more complex. Users often get lost in a lot of commodity information space, and can not find the goods which they need.Current mainstream e-commerce recommendation system: collaborative filtering algorithm based e-commerce recommendation system; statistical algorithm based on user recommendation system;knowledge discovery algorithm based recommendation system and utility-based e-commerce recommendation system. Consumers intention recognition gradually become the main technology in the electronic commerce development driving force. 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 an 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 the11-20% accuracy improvement shows good, realistic predictions.