Attention Mechanism Model Combined with Adversarial Learning for E-commerce User Behavior Classification and Personality Recommendation

Abstract

In traditional e-commerce websites, consumers’ evaluation of products will affect new customers’ decisions on whether to buy the products. Some fraudulent merchants manipulate consumers’ online comments for their interests, and multitudes of fake comments abuse consumers’ rights and interests and the development of traditional e-commerce. The purpose of the present work is to detect and identify fake comments through user behavior classification. A series of innovative research works are carried out around the user behavior recognition task from four aspects: extraction and description of low-level behavior features, spatial representation of high-level user behavior, design of behavior classification model, and user behavior detection in unsegmented text. A feature extraction model based on the super-complete independent component analysis algorithm and a behavior classification model via attention mechanism are proposed. Moreover, a feature source discriminator is designed, and adversarial learning is used to optimize discriminator loss and generator loss. Finally, an experiment is implemented to test the effects of attentional mechanism and adversarial learning on the text retrieval model and visualize the results. In this experiment, the text retrieval algorithm based on a stacked cross-attention mechanism and adversarial learning retrieves the Microsoft Common Objects in Context (MS-COCO) and Flickr30K data sets on mainstream transmedia. The experimental results demonstrate that the stacked cross-attention mechanism has an excellent matching ability of fine-grained hierarchical features; the average accuracy of the algorithm after improvement increases from 81.23% to 83.11%. Besides, the prediction accuracy coverage is above 95%, which can significantly improve the predicted effect of text characteristics and image features, thus enhancing the accuracy of text retrieval and classification. The research has a certain experimental reference value for the classification and discrimination of business users’ behavior.