With the continuous advancement of digitalization in the retail industry, massive user behavior data and product information have created new possibilities for precision marketing, inventory optimization, and personalized recommendations, but at the same time, they have also triggered a series of problems. Faced with diverse sources and structures of data, as well as constantly changing customer flow and complex consumer preferences, traditional rule-based retail systems often respond slowly and have limited recognition accuracy. In response to this situation, this study has developed an intelligent management solution suitable for retail scenarios. This scheme integrates convolutional neural networks, recurrent neural networks, and attention computing mechanisms, and designs core components for product image recognition, user behavior prediction, and price dynamic adjustment, respectively. These components have been validated in real and simulated data environments. This system can integrate online and offline information channels and support intelligent control throughout the entire process from customer entry to payment completion. Experiments have shown that compared with fixed rule-based engines, traditional machine learning methods, and single structure deep learning models, this approach has improved in multiple indicators: the click through rate of recommended content has increased by about 18% to 26%, inventory turnover has increased by about 14% to 20%, and revenue growth achieved through dynamic pricing strategies has increased by about 12% to 17%.
Guangsong Jia (Thu,) studied this question.
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